Question: What distance function and transfer function do the author use for their method?

Your answer should be a sentence describing the distance function and transfer function used by the author for their method, including the formulas of the functions.

The author use distance function $d(u,v) = -u^Tv/\tau$ and transfer function $f(h) = \frac{h}{||h||}$ for their method.

Question: What distance function and transfer function do the author use for their method?


Question: What datasets were used to train the default embedding model for the retriever in the experiment of the paper?

Your answer should be a python list of the dataset names, e.g. ["dataset1", "dataset2", ...]. YOU MUST USE THE EXACT NAMES FROM THE PDF WITHOUT CHANGING THE CAPITALIZATION.


Question: For the dataset where "Before we dive into the answer." performs the best in specific setting in the paper that proposes IAP, what deficiencies of previous datasets were raised by the authors of the dataset? Additionally, what variations did the authors propose to address these deficiencies?

Your answer should be a Python list of 2 elements, the first is a string, the deficiencies, the second is a python list of strings, the categories of variations in general as proposed in the paper.

A large number of the problems in ASDiv-A and MAWPS can be correctly answered without even looking at the question. This suggests the presence of patterns in the bodies of MWPs in these datasets that have a direct correlation with the output equation.

Question: What deficiencies of previous datasets were raised?

Question Sensitivity
Reasoning Ability
Structural Invariance


Question: which term is mentionned in this paper ("WINOPRON: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Case")in terms of the result that Smaller FLAN-T5 models perform at chance level? What evaluation contrasts does the source paper of this term investigate?

Your answer should be a single python list like ["string1", "string2"]. The first string should be the name of the term. The second string should be about the evaluation contrasts.

production vs. forced choice, and metalinguistic judgment vs. probability measurement

Question: What evaluation contrasts does the source paper of this term investigate?


Question: Which paper first published a real-world Chinese-English text image translation dataset?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first published a real-world Chinese-English text image translation dataset?

Exploring Better Text Image Translation with Multimodal Codebook


Question: How can we get the bias attribution without skill knowledge according to formula (2)?

Your answer should be a Python strings of the calculation method of bias attribution without skill knowledge.

The bias attribution without skill knowledge can be calculated by the difference of between the the attribution score between the output text ^y and the golden label text y. Because A(i,x,^y)(h) includes skill knowledge in addition to biased knowledge since estimating the biased text also contains the knowledge of language modeling, such as understanding instruction knowledge. Therefore, we should disentangle the skill knowledge to compute the clean bias attribution.

Question: How can we get the bias attribution without skill knowledge according to formula (2)?


Question: What is the meaning of formula (1) in the paper?

Your answer should be a python strings concisely explaining the meaning of the formula.

Formula (1) is the frame-by-frame concatenation operation that concatenates the encoding results of Whisper and BEATs along the feature dimension. The Whisper model is trained for speech recognition and translation based on a large amount of weakly supervised data, whose encoder output features are suitable to model speech and include information about the background noises. BEATs is trained to extract high-level non-speech audio semantics information using iterative self-supervised learning. The input audio is first tokenised then masked and predicted in training. The tokeniser is updated by distilling the semantic knowledge of the audio tokens. Therefore, the resulting auditory features of these two encoders are complementary and suitable for general audio inputs with both speech and non-speech information.

Question: What is the meaning of formula (1) in the paper?


Question: In the paper "DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads", on which two benchmarks was the long text capability tested? Which institutions proposed the Retrieval head used in this paper?

Your answer should be a python list of two items. The first item is a python list of two strings, the two benchmarks. The second item is a python list strings, the institutions.

MIT
Tsinghua University
SJTU
University of Edinburgh
NVIDIA


Question: Which paper propose a PEFT method for LLM that detects important attention heads first, then adds learnable bias to their outputs?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper propose a PEFT method for LLM that detects important attention heads first, then adds learnable bias to their outputs?

LoFiT: Localized Fine-tuning on LLM Representations


Question: What are the proportions of the training and test sets for the two benchmark datasets used in this paper?

Your answer should be a python dictionary as {"dataset1": proportion1, "dataset2": proportion2}. The proportions should be rounded to two decimal places.


Question: What is the formula of the loss function used to align the feature spaces of the visual and text transformers in this paper?

Your answer should be a python strings about the exact formula given in the reference paper, you don't need to explain the variables in the formula, e.g., "loss_formula".

\mathcal{L}_{itc} = \frac{1}{2} \mathbb{E}_{(I,T)\sim D}[H(\mathbf{y}^{i2t}(I), \mathbf{p}^{i2t}(I)) + H(\mathbf{y}^{t2i}(T), \mathbf{p}^{t2i}(T))]

Question: What is the formula of the loss function used to align the feature spaces of the visual and text transformers in the anchor PDF?


Question: Among all the methods tested on IHS and SoyVein500, which methods have fewer parameters than the new method proposed in the paper?

Your answer should be a python list about the names of the methods, e.g., ['method1', 'method2']. YOU MUST USE THE EXACT NAMES FORM THE PAPER.


Question: According to the paper that proposed the evaluation method which the BASALT benchmark specifies as methodological best practices, how is the match quality criterion defined?

Your answer should be a Python string, the formula in LaTeX format.

q_{\text{draw}} \left( \beta^2, \mu_i, \mu_j, \sigma_i, \sigma_j \right) := \sqrt{\frac{2\beta^2}{2\beta^2 + \sigma_i^2 + \sigma_j^2}} \cdot \exp \left( -\frac{(\mu_i - \mu_j)^2}{2 \left( 2\beta^2 + \sigma_i^2 + \sigma_j^2 \right)} \right)

Question: How is the match quality criterion defined in the TrueSkill paper?


Question: In the intervention-based training of this paper(titled "Inducing Character-level Structure in Subword-based Language Models with Type-level Interchange Intervention Training"), can you give me a github link of the method mainly used in this part?

Your answer should be a single link like "https://github.com/a/b".


Question: What is the most appropriate evaluation metric for this paper?

Your answer should be a python strings of the exact name of the evaluation metric.


Question: According to the RaIR paper, which algorithm performs the best in the objects flipped setting in Construction? In the first phase of that algorithm, what's the loss function?

Your answer should be a Python list of 2 strings, the first is the abbreviation of the algorithm, and the second is the formula in LaTeX format.

\mathcal{L}_m = \left\| \Delta s_{t+1} - f_{\theta_m}(s_t, a_t) \right\|_2^2

Question: What's the loss function?


Question: How does R-Div do to address the overfitting issue of H-Div?

Given two datasets, R-Div searches a minimum hypothesis on their mixed data and evaluates its empirical risks on each individual dataset.

Question: How does R-Div do to address the overfitting issue of H-Div?


Question: In Table 1: Test MAE for different interpolation methods,which method performs better in dataset Shallow Water with grid Coarser?

Your answer should be one of k-NN,Linear and IDW.


Question: Is there any paper that performs adversarial training on frame level for audio-visual representation learning?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there any paper that performs adversarial training on frame level for audio-visual representation learning?

MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition


Question: Which paper first use the attention weights to guide the simultaneous inference of speech translation models?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first use the attention weights to guide the simultaneous inference of speech translation models?

Attention as a Guide for Simultaneous Speech Translation


Question: What are the main innovations of this paper?

Your answer should be a python strings concisely introducing the main innovations.

In the VMoE architecture, the token-expert matching process is formulated as a regularized optimal transport problem, solved using the Sinkhorn algorithm.

Define the gating matrix for regularized OT as \Pi_{OT}=\arg \min_{T\in u(a,b)} <T, -L>+ \frac{1}{2k}\left \| T \right \| _2^2, a=1_e, b = (t/e)1_e.

Question: What are the main innovations of this paper?


Question: Which model does this paper("On the Similarity of Circuits across Languages: a Case Study on the Subject-verb Agreement Task") use?For the series of this model, what is the other one with different size?

Your answer should be a single list of two strings, every string is a model name,e.g., ["modelname 7k","modelname 3B"]


Question: Why the paper "Debiasing Large Language Models with Structured Knowledge" proposes a new Bias Score? What's the formula of the original Score?

Your answer should be Python list of two strings, the first is the reason why the authors propose a new Bias Score. The second is the formula in LaTeX format.

The CrowSPairs score represents the percentage of instance pairs in which the stereotypical sentence has lower perplexity than the anti-stereotypical sentence in the total number of instance pairs. Since it is not easy to understand that the scores closer to 50 indicate less bias, we instead utilized the Bias score, which is defined as equal to |CrowSPairs score - 50 |, to replace the CrowSPairs score. A lower score represents lower bias.

Question: Why the anchor PDF proposes a new Bias Score?

\text{score}(S) = \sum_{i=0}^{|C|} \log P(u_i \in U \mid U_{\setminus u_i}, M, \theta)

Question: What's the formula of the original Score?


Question: What paper compares humans' and language models' non-literal interpretations of utterances featuring phenomena like deceit, irony, and humor?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: What paper compares humans' and language models' non-literal interpretations of utterances featuring phenomena like deceit, irony, and humor?

A fine-grained comparison of pragmatic language understanding in humans and language models


Question: In the paper that proposed the method applied by SEVA dataset for automatic sketch generation, what's its only disadvantage compared to other sketch generation algorithms?

Your answer should be a string, a brief summary of the disadvantage.

CLIPasso can't produce a sequential sketch.

Question: What's the only disadvantage of CLIPasso, compared to existing sketch generation algorithms?


Question: Which implicit bias mentioned in the paper "Benchmarking Cognitive Biases in Large Language Models as Evaluators" is not addressed in the works that directly overlap with the paper, particularly those exploring LLMs' capabilities as evaluators?

Your answer should be a string, the name of the implicit bias as mentioned in the anchod PDF.


Question: Which paper presents an easy to implement and high performing method for OOD detection with language models?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper presents an easy to implement and high performing method for OOD detection with language models?

Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection


Question: What are the meanings of different loss items in equation (9)? i.e. $\mathcal{L}_{c}^{A}$, $\mathcal{L}_{s}^{A}$, $\mathcal{L}_{a}^{A}$, and $\mathcal{L}_{c}^{B}$

Your answer should be a Python list of four elements, where each of the item represent the meaning of the corresponding loss item.

$\mathcal{L}_{c}^{A}$: The content reconstruction loss, which ensures that the model can accurately reconstruct the input text from the latent representations.

$\mathcal{L}_{s}^{A}$: The stance classification loss, which guides the model to predict the correct stance of the text. It typically uses cross-entropy loss to align predictions with true stance labels.

$\mathcal{L}_{a}^{A}$: The aspect span reconstruction loss, which helps the model generate a specific aspect-related text span using a Gaussian negative log-likelihood (NLL) loss.

$\mathcal{L}_{c}^{B}$: The swapped reconstruction loss, derived from a swapping autoencoder mechanism. It ensures that the disentanglement is maintained even after swapping aspect embeddings across samples with the same aspect but different stances.

Question: What are the meanings of different loss items in equation (9)? i.e. $\mathcal{L}_{c}^{A}$, $\mathcal{L}_{s}^{A}$, $\mathcal{L}_{a}^{A}$, and $\mathcal{L}_{c}^{B}$


Question: What are the main components of ERRA model?

Your answer should be a python list of strings, every element of the list is the name of the component directly mentionned in this paper.

Retrieval Enhancement
Aspect Enhancement
Joint Enhancement Transformers


Question: Could you suggest research that examines the effects of starting language models with weights from pretrained nondiffusion models on the convergence behavior of diffusion losses?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Could you suggest research that examines the effects of starting language models with weights from pretrained nondiffusion models on the convergence behavior of diffusion losses?

SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control


Question: For the model used in the paper's experiments that achieved the best results on authoritative Chinese and English benchmarks of the same size, which categories of data accounted for more than 9% of the model's training data?

Your answer should be a python list of strings and the strings should be categorie names. The specific name are based on the relevant paper.


Question: In CSCD-NS, what's the base model of the detection model for data selection? How many hours does it roughly take for the base model to reach at least 85 on GLUE?

Your answer should be a Python list of 2 elements, the first is the name of the model, the second is the number of hours it takes.


Question: In the Sequence Mixer of the Monarch Mixer, what are the two types of convolution components?

Your answer should be a python list of two strings


Question: What baselines do authors use? And according to Table 2, which baseline can reach the best performance?

Your answer should a list with two items, the first item is a python list of the name of baselines, and the second item is the name of baseline reaching the best performance, e.g. [[baseline 1, baseline 2, ...], baseline 1].


Question: What do $\gamma_t$ and $\sigma$ mean in the formula(2) in RulE paper?

Your answer should be a python list of two strings, explaining the meaning of $\gamma_t$ and $\sigma$ respectively

$\gamma_t$ is a fixed triplet margin.
$\sigma$ is the sigmoid function.

Question: What do $\gamma_t$ and $\sigma$ mean in the formula(2) in RulE paper?


Question: In the background section of this paper, under the topic Isotropy of PLMs, what is the main problem of PLMs? How is this problem defined in the paper introducing it?

Your answer should be a python strings about the main problem of PLMs and how it is defined in the paper introducing it.

The main problem of PLMs is the representation degeneration or anisotropy problem. The learned embeddings occupy a narrow cone in the vector space, which largely limits their expressiveness. Researches find that language models trained with tied input/output embeddings lead to anisotropic word embeddings, and the singular values of the word embedding matrix decay drastically. In other words, except for a few dominating singular values, all others are close to zero.

Question: In the background section of this paper, under the topic Isotropy of PLMs, what is the main problem of PLMs? How is this problem defined in the paper introducing it?


Question: In the AgentTuning paper, what does the reward $r$ stand for? What role does it play in the subsequent process? Does it stand for the same meaning in the AgentBank paper? Does it play the same role?

Your answer should be a string, containing the answers to the 4 sub-questions.

According to the AgentTuning paper, each trajectory has a final reward $r \in [0, 1]$, reflecting the completion status of the task.

Recall that each interaction trajectory receives a reward $r$, this allows us to automatically select high-quality trajectories based on the reward.

Yes. According to the AgentBank paper, finally, a final reward $r \in [0, 1]$ is returned depending on the task completion status.

No. The AgentBank paper does not use the reward $r$ to do anything.

Question: In the AgentTuning paper, what does the reward $r$ stand for? What role does it play in the subsequent process? Does it stand for the same meaning in the AgentBank paper? Does it play the same role?


Question: In the paper that introduces a novel task "source imputation", an ODE integrator is used in Algorithm 1. In the paper that proposed this ODE integrator, what's the original form of Eq. 4 in terms of $f(t)$ and $g(t)$?

Your answer should be a string, the formula in LaTeX format.

\mathrm{d}\boldsymbol{x} = \left[ f(t)\boldsymbol{x} - \frac{1}{2} g(t)^2 \nabla_{\boldsymbol{x}} \log p\left(\frac{\boldsymbol{x}}{s(t)}; \sigma(t)\right) \right] \, \mathrm{d}t.

Question: What's the original form of Eq. 4 in terms of $f(t)$ and $g(t)$?


Question: What paper first adapted ControlNet to generate continuous videos in a training-free manner?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: What paper first adapted ControlNet to generate continuous videos in a training-free manner?

ControlVideo: Training-free Controllable Text-to-Video Generation


Question: Which paper was the first to propose combining human spoken language and sign language datasets with gloss annotations to enhance the performance of sign language translation?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper was the first to propose combining human spoken language and sign language datasets with gloss annotations to enhance the performance of sign language translation?

Neural Machine Translation Methods for Translating Text to Sign Language Glosses


Question: What work first uses LLM to code robotic simulation tasks and show sim-to-real benefits with policy pre-training in simulation?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: What work first uses LLM to code robotic simulation tasks and show sim-to-real benefits with policy pre-training in simulation?

GENSIM: GENERATING ROBOTIC SIMULATION TASKS VIA LARGE LANGUAGE MODELS


Question: On which language does LLaMA-2 13B with no removal reaches its second highest perplexity?

Your answer should be a word DIRECTLY FROM THE PDF WITHOUT ANY EXPLANATION.


Question: Which paper first propose to mask positions to pre-train multi-modal document transformer?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first propose to mask positions to pre-train multi-modal document transformer?

LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding


Question: Can you tell me the github link for GC-Bench library?

Your answer should be a single link string, e.g. "https://github.com/a/b"


Question: In Equation (1), what does the interval [:2] mean?

Your answer should be a Python string, explaining with a brief sentence.

The interval [:2] means that the input to the TIRE module only passes through the first two blocks of the encoder.

Question: In Equation (1), what does the interval [:2] mean?


Question: Which dataset is the downstream task with the largest training set in BigDocs-Bench curated from, and where do the samples originally come from?

Your answer should be a Python list of two strings, the first is a single word, the name of the dataset, the second is a sentence or a phrase, the source of the samples.

These samples come from publicly available repositories on GitHub.

Question: Where do the samples originally come from?


Question: In the english dataset where HGALayoutLM reaches new SOTA, what's the evaluation metric for text recognition? Additionally, what's the formula for that metric?

Your answer should be a Python list of two strings, the evaluation metric and the formula for that metric. Note that you should output the formula in the LaTex format.

Question: What's the formula for Levenshtein similarity?


Question: Is there a paper that takes a mixed machine learning and solver based approach to code translation?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there a paper that takes a mixed machine learning and solver based approach to code translation?

GUESS & SKETCH: LANGUAGE MODEL GUIDED TRANSPILATION


Question: Which sampler was implemented in the paper which proposed the latent-space diffusion model used in the paper "UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models"? What is the relationship between that sampler and UniPC according to this paper?

Your answer should be brief text giving the name of the sampler and the relationship between that sampler and UniPC.

The sampler is DDIM, and UniPC will reduce to DDIM when p=1, where p is the order of accuracy.

Question: Which sampler was implemented in the paper which proposed the latent-space diffusion model used in the paper "UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models"? What is the relationship between that sampler and UniPC according to this paper?


Question: For the task of violence detection, the comments are classified into some distinct categories:Direct Violence, Passive Violence, NonViolence and so on. In the most relevant paper, how is Passive Violence defined?

Your answer should be a single python string.

In this category, instances of violence are represented by the use of derogatory language, abusive remarks, or slang targeting individuals or communities. Additionally, any form of justification for violence is also classified under this category.

Question: For the task of violence detection, the comments are classified into some distinct categories:Direct Violence, Passive Violence, NonViolence and so on. In the most relevant paper, how is Passive Violence defined?


Question: Which paper first combines different methods for uncertainty quantification in one?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first combines different methods for uncertainty quantification in one?

Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks


Question: Which paper first proposes a unified framework for black-box and white-box detection of AI-written text with explanations?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first proposes a unified framework for black-box and white-box detection of AI-written text with explanations?

DNA-GPT: DIVERGENT N-GRAM ANALYSIS FOR TRAINING-FREE DETECTION OF GPT-GENERATED TEXT


Question: What's the relationship between MINT and MINT-1T?

Your answer should be a paragraph, illustrating the relationship between MINT and MINT-1T.

MINT and MINT-1T don't have direct relationship.

MINT is short for "multi-turn interactions", while MINT in MINT-1T is short for "Multimodal INTerleaved".

Question: Can you explain the different types of machine learning?


Question: Can you find a research paper that discusses using structured pruning techniques to scale down language models, where the original model being pruned has billions of parameters?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Can you find a research paper that discusses using structured pruning techniques to scale down language models, where the original model being pruned has billions of parameters?

SHEARED LLAMA: ACCELERATING LANGUAGE MODEL PRE-TRAINING VIA STRUCTURED PRUNING


Question: In table 2 of the paper titled "ORDERED GNN: ORDERING MESSAGE PASSING TO DEAL WITH HETEROPHILY AND OVER-SMOOTHING", which model gets the best performance on CiteSeer dataset? What question does the source paper of this model mainly focus on?

Your answer should be a single python list like ["model_name", "question"].

Can the aggregation on a graph benefit from a continuous latent space, such as using geometry in the space to build structural neighborhoods and capture long-range dependencies in the graph?

Question: What question does the source paper of this model mainly focus on?


Question: Among all the given anchor PDFs, which one does not use any objective evaluation metrics? What is the difference in conversion goals between this paper and the others?

Your answer should be a Python list where the first item is the full name of the paper (a string) and the second item describes the difference in conversion goals (a string).

Voice-Preserving Zero-Shot Multiple Accent Conversion

Other papers focus on voice conversion, while this paper focuses on accent conversion, where the speaker's voice identity should be preserved.

Question: What is the difference in conversion goals between this paper and the others?


Question: Which downstream tasks does CLiCoTEA outperform other models in terms of zero-shot performance on the IGLUE benchmark?

Your answer should be a Python list of strings, every string is the abbreviation of a downstream task type mentioned in the paper.


Question: For the four types of evaluation mentioned in the paper, provide their names and corresponding overall score range.

Your answer should be a Python dictionary, where the keys are the names of the four types of evaluation and the values are the corresponding overall score range. e.g. {"evaluation1": [-1, 1], "evaluation2": [0, 0.5], ...} . YOU MUST USE THE EXACT NAMES FROM THE PDF WITHOUT CHANGING THE CAPITALIZATION.


Question: In the experiment presented in Table 1 of the paper "Asynchronous Perception Machine for Efficient Test Time Training", which teacher model performs the best?

Your answer should be a python strings. YOU MUST USE THE EXACT NAME FROM THE TABLE IN THE PAPER.


Question: Which database did the authors use to select the 250 highest-rated television series for building their dataset?

Your answer must be ONE string just containing the database's name in abbreviation format.


Question: In the prompt that Puzzler adopts, what's the exact context of Task Description for Event Detection?

Task Description: Given an input list of words, identify all triggers in the list, and categorize each of them into the predefined set of event types. A trigger is the main word that most clearly expresses the occurrence of an event in the predefined set of event types.


Question: What paper proposes breaking down programming problems by predicting the objects that a solution would create?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: What paper proposes breaking down programming problems by predicting the objects that a solution would create?

ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis


Question: How many websites are included in WebArena? Which are they?

Your answer should be a Python list of 2 elements. The first one is an integer indicating the number of the websites included in WebArena. The second one is a string list storing the website names.

e-commerce platform
social forum platform
collaborative development platform
content management system


Question: Which type of error is most common on StrategyQA dataset across models according to the paper?

Your answer must be ONE string of the error's name. You can choose from 'Error#1', 'Error#2', 'Error#3' and 'Error#4'.


Question: What is the source of the datasets used in the paper?

Your answer should be a Python list, where each element is a string of a dataset source.

Enron Email Corpus
New Yorker
Amazon Review Subset


Question: Is there a paper that shows that language models' error distribution is different for unfamiliar entities that is not apparent when models are evaluated on familiar entities alone?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there a paper that shows that language models' error distribution is different for unfamiliar entities that is not apparent when models are evaluated on familiar entities alone?

Factual or Contextual? Disentangling Error Types in Entity Description Generation


Question: Is there any paper that uses prompt tuning in multi-answer QA?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there any paper that uses prompt tuning in multi-answer QA?

Answering Ambiguous Questions via Iterative Prompting


Question: Is there a method that measures the information provided in a (model generated) rationale beyond what the original context provided?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there a method that measures the information provided in a (model generated) rationale beyond what the original context provided?

REV: Information-Theoretic Evaluation of Free-Text Rationales


Question: Which stage of the two stage training mentioned in the paper is the labeled data used in?

The labeled data is used in the fine-tuning stage

Question: Which stage of the two stage training mentioned in the paper is the labeled data used in?


Question: Which metric is used to evaluate the emergent abilities on BIG-Bench datasets, as is indicated in the anchor pdf? And why this metric is not favorable according to this paper?

Your answer should be a Python list of two elements, where the first element is the name of the metric, and the second is the reason why this metric is not favorable according to this paper.

Question: Why PPL is not favorable according to this paper?

Because ppl of correct options and incorrect options may decrease simultaneously.


Question: What new methods were proposed for multi-perspective mathematical augmentation in the dataset used for Stage-1 training in the paper "MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning"?

Question: What new methods were proposed for multi-perspective mathematical augmentation in the dataset used for Stage-1 training in the paper "MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning"?

Reorganization. After the reorganization through the LLM, the solving steps will be more logically organized and clearer. Phrases such as "understand the problem", "define variables", and "calculate the number" act as explicit instructions, leading us toward the final result by "The answer is".

Backward-Forward Transformation. For a certain question-answer pair, we firstly utilize FOBAR to transform the original question Q into a backward one Q_b; secondly, we rephrase the FOBAR question into a new form where the masked value is requested directly instead of employing an unknown variable X, resulting in a "secondary forward" question which we called BF-Trans question, marked as Q_{bf}. Finally, we generate the solution S_{bf} for this BF-Trans question. Collecting all these BF-Trans augmented samples, we can have D_{bf} = {(Q_{bf} , S_{bf})}.

Expression Replacement. First extract all mathematical expressions from the solution. Subsequently, an arithmetic expression is altered to form a novel equation. With the original problem statement and new equations as guides, a new question can be generated denoted as Q_{replace}.

Majority Sampling Finetuning. They utilize majority voting with k = 30 to request solutions and only select one response with the majority answer for finetuning.

Nested Multi-task Learning. For the main task of solving mathematical problems Q, they select two auxiliary tasks: summarizing the question and listing the solving plan. They prepend the text of question outline O, solving plan P , or both to the solution text S, assembling into an individual final solution S_{mt} = O \oplus P \oplus S, for each original question. Then they have D4 = {(Q, S_{mt})} as the nested multitask dataset.


Question: In the latest retrieval method that is applied in the experiment of the case study, and that doesn't require re-ranking model, which dataset is used as training data for tool learning?

Your answer should be a string, the name of the dataset.


Question: To investigate the problem "Can the generative model be used to effectively leverage (small amounts of) human data, and also combine it with simulated agents?", which baseline is applied? In the paper that proposes the baseline, what's the average learning rate for the baseline?

Your answer should be a Python list of 2 elements, the first is a string, the name of the baseline, the second is a float, the average learning rate, rounded to 4 decimal places.


Question: According to this paper, what type will the question "How to keep strawberries fresh" be classified into?

Your answer should be a single string representing the question type.


Question: What's the difference between DigiRL and a trivial filtered BC?

Your answer should be in fluential English.

Filtered BC filters trajectories by golden rewards.

DigiRL filters trajectories by CRR Advantage.

Question: What's the difference between DigiRL and a trivial filtered BC?


Question: Which newest paper about fine-tuning strategies does this paper(titled "ICU: Conquering Language Barriers in Vision-and-Language Modeling by Dividing the Tasks into Image Captioning and Language Understanding") mention in the troduction? What is the originality of this paper(I mean the paper mentioned in the "ICU" paper) compared to previous works?

Your answer should be a single python list, the first element is the string of the paper name, the second element is a string of the originality of this paper.

Delving Deeper into Cross-lingual Visual Question Answering

Question: Which newest paper about fine-tuning strategies does this paper(titled "ICU: Conquering Language Barriers in Vision-and-Language Modeling by Dividing the Tasks into Image Captioning and Language Understanding") mention in the troduction?

This paper is the first to provide a comprehensive analysis of multilingual VQA, with a focus on cross-lingual transfer.

Question: What is the originality of this paper compared to previous works?


Question: Is there a study that shows how to help the demonstration retriever better integrate feedback from LLMs?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there a study that shows how to help the demonstration retriever better integrate feedback from LLMs?

Unified Demonstration Retriever for In-Context Learning


Question: What are the main works at the table-level when performing data augmentation on the MMTab dataset in this paper?

Your answer should be a Python strings about the main works at the table-level when performing data augmentation on the MMTab dataset.

The main work is to separately design scripts to render table images with three different styles: Web-page (70.8%), Excel (19.4%) and Markdown (9.8%). Fine-grained adjustments such as font type and cell colors are also considered

Question: What are the main works at the table-level when performing data augmentation on the MMTab dataset in this paper?


Question: When the authors use ChatGPT in generating data points, what is the templated prompt?

Your answer should be raw text directly demonstrating the used prompt template without any other context.

I am creating a dataset and need to generate data that is similar but not identical to the following examples. Here are 5 examples from my dataset:
1. [Example 1]
2. [Example 2]
3. [Example 3]
4. [Example 4]
5. [Example 5]
Please generate [Specified Number] new data points that are similar in style and structure to these examples but are unique in content. Format the responses as a numbered list, starting from 6 onwards. Each data point should start on a new line and be prefixed with its corresponding number followed by a period and a space.
For example:
6. [New Data Point 1]
7. [New Data Point 2]
...

Question: When the authors use ChatGPT in generating data points, what is the templated prompt?


Question: Which paper studies how difficult is a policy learning problem under non-additive rewards in terms of theoretical lower bounds?

Your answer should be the title of the paper WITHOUT ANY OTHER EXPLANATION.

Question: Which paper studies how difficult is a policy learning problem under non-additive rewards in terms of theoretical lower bounds?


Question: What is the ranking of the average performance of the models compared in the experiment across all languages where each model has a value in the all-language finetuning, from highest to lowest?

Your answer should be a Python list of elements, each element is a model name string, e.g., ["model_name 1", "model_name 2", ...].


Question: In this paper, for Gitksan and Natugu, what is the test set? What's its relevant github link?

Your answer should be a single list of two strings, the first string is the test set name, the second string is the github link.

SIGMORPHON 2023 Shared Task on Interlinear Glossing


Question: Which paper considers both weights and activations when pruning large language models?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper considers both weights and activations when pruning large language models?

A SIMPLE AND EFFECTIVE PRUNING APPROACH FOR LARGE LANGUAGE MODELS


Question: According to Table 1, which setup (including its corresponding parameters) achieves the highest precision on the test set?

Your answer should be a python dict, with the format of {'setup_name': parameter_value}.


Question: In paper "What Do Language Models Learn in Context? The Structured Task Hypothesis.", which hypothesis is mostly supported by the experiments?

Your answer should be the context that defines the hypothesis.

During pre-training, an LLM learns a set of tasks $\bar{\mathcal{T}}$. At inference time, the LLM uses the demonstration to compose a sequence of learned tasks $\tau_1,\tau_2,\dots\in\bar{\mathcal{T}}$ and uses this composition for prediction. The composition itself may result in a novel task not seen during pre-training.

Question: In paper "What Do Language Models Learn in Context? The Structured Task Hypothesis.", which hypothesis is mainly supported by the experiments?


Question: Which model achieves the highest accuracy of the classification when the training data consists of 512 pairs of FPQ and TPQs in this paper?

Your answer should be a python string about the name of the best model. You"d better use the names as they are referred to in the paper.


Question: What paper investigated the effect of the relative position (closer or further away) of the most pertinent retrieved code snippets on repository-level code completion performance?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: What paper investigated the effect of the relative position (closer or further away) of the most pertinent retrieved code snippets on repository-level code completion performance?

RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems


Question: In the paper that proposes LaCLIP, under zero-shot setting, pretrained with CC12M, LaSLIP lags behind SLIP on which specific evaluation dataset? In that dataset, which country has the highest proportion?

Your answer should be a Python list of 2 string, the abbreviation of the dataset and the name of the country, as given in the papers.


Question: What are the main questions that this paper tries to resolve or answer?

Your answer should be a Python list of text strings, with each element being one critical problem that this paper analyzes, e.g., ["question 1", "question 2", ...].

Is it possible to determine data contamination by solely analyzing the inputs and outputs of existing LLMs?

Do recent GPTs excel in Text-to-SQL tasks in a zero-shot setting both on potentially leaked data and totally unseen one?

Is data contamination affecting the accuracy and reliability of an existing GPT in Text-to-SQL tasks?

Question: What are the main questions that this paper tries to resolve or answer?


Question: According to the EBD paper, which model's forward process is based on the PDE $\frac{\partial}{\partial t} \mathbf{x}(i,j,t) = \Delta \mathbf{x}(i,j,t)$? In the paper that proposes that model, how is $\tilde{u}$ calculated?

Your answer should be a Python list of 2 elements. The first is the abbreviation of model, the second is a string, the formula in LaTeX format.

Question: In the paper that proposes IHDM, how is $\tilde{u}$ calculated?


Question: In the paper that proposed PsychoBench, a method is applied to jailbreak GPT-4. In the paper that proposed the jailbreak method, for GPT-3.5-Turbo, in Chinese, on which domain the Unicode baseline surpasses the proposed method?

Your answer should be a Python string, the name of the domain.


Question: In the paper "SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark",how many free-response questions are there in the 6 benchmarks listed in Table 1? Also, please give the total number of free-response questions in the 6 benchmarks.

Your answer should be a python dictionary, e.g. {'total': 10, 'dataset_1': 2, 'dataset_2': 3, ...}. For the names of datasets, use the exact names in Table 1 without changing CAPITALIZATION.


Question: Which paper proposed dictionary-based Bayesian inference to improve the performance of image text matching model?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper proposed dictionary-based Bayesian inference to improve the performance of image text matching model?

Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information


Question: Can we find the solution of the Bilevel optimization when the lower-level problem is nonconvex?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Can we find the solution of the Bilevel optimization when the lower-level problem is nonconvex?

On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation


Question: What are the four evaluation metrics of the held-out dataset that InstructBLIP used for image QA?

Your answer should be a Python list of 4 strings, the metrics.


Question: What is the main Implicit K-means loss used in this paper(titled "THE HIDDEN UNIFORM CLUSTER PRIOR IN SELF-SUPERVISED LEARNING")? How is the loss between two batches defined in the source paper of this loss?

Your answer should be a single python list like ["xxx loss","some formulas in latex format"]

\ell\left(Z, Z^{\prime}\right) = \lambda s\left(Z, Z^{\prime}
ight)+\mu\left[v(Z)+v\left(Z^{\prime}\right)\right]+
u\left[c(Z)+c\left(Z^{\prime}\right)\right]

Question: How is the loss between two batches defined in the source paper of this loss?


Question: What research first proposed a new kind of cascaded diffusion of a Markov process?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: What research first proposed a new kind of cascaded diffusion of a Markov process?

RELAY DIFFUSION: UNIFYING DIFFUSION PROCESS ACROSS RESOLUTIONS FOR IMAGE SYNTHESIS


Question: RRCP is a new pipeline proposed to recognize retrieval complexity, on which complex QA datasets does it significantly outperform the LLM baseline, with a clear improvement in Accuracy or F1 Score of at least 0.1?

Your answer should be a list of elements, each element is the QA dataset name string, e.g., ["QA dataset1", "QA dataset2", ...].


Question: In the related work mentioned in the Table 1 of the paper Reflect-RL, that is categorized as RL Fine-tuning and that doesn't involve vision modal, what's the token-level probability of a_k?

Your answer should be a formula in LaTeX format.

P_{\text{token}}(a_k \mid s) = P(w_k^1, \ldots, w_k^{N_k} \mid s) = \prod_{i=1}^{N_k} P(w_k^i \mid s, w_k^1, \ldots, w_k^{i-1})

Question: What's the token-level probability of a_k?


Question: What's the formula of Subset consistency accuracy?

Your answer should be a single latex formula extracted from the source paper of this metric.

\operatorname{ACC}(I) \stackrel{	ext { def }}{ = } \sum_{m_{1}, m_{2} \in \mathcal{M}} rac{\mathbb{1}\left[\left(m_{1}>_{I} m_{2}
ight) \Leftrightarrow\left(a_{m_{1}}>a_{m_{2}}
ight)
ight]}{|\mathcal{M}|^{2}}

Question: What's the formula of Subset consistency accuracy?


Question: What are the main differences between Mobile-Agent-v2 and Mobile-Agent?

Your answer should be in a well-formated item list.

Mobile-Env-v2 takes advantage of coordination of multiple agents, while Mobile-Env comprises only a single agent.

Mobile-Env-v2 introduces a memory unit to save experiences from past tasks.

Question: What are the main differences between Mobile-Agent-v2 and Mobile-Agent?


Question: According to Table 2, which models perform the highest on each of the 8 tasks of GLUE?

Your answer should a python list of the name of models reaching highest performance on MNLI, QQP, QNLI, SST-2, STS-B, MRPC, RTE, and CoLA respectively. If two models get the same score, you can use "and" to connect their names, e.g. A and B.

SCALEARN++
SCALEARNUNIFORM and SCALEARNUNIFORM++
SCALEARNUNIFORM and SCALEARNUNIFORM++
SCALEARN++
SCALEARN
ADAPTERFUSION
SCALEARN
SCALEARN++


Question: In the comparison of APoLLo with SOTA methods on a Base-to-Novel Class Generalization task, what is the title of the source paper of the dataset where APOLLO outperforms MaPLe(previous SOTA) the most?


Question: How many thousand conversations are there in the datasets used to train CONVAUG in total?

Your answer should be a python float with one decimal places.


Question: Which paper first proposed shared adapter module across layers?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first proposed shared adapter module across layers?

One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning


Question: In the paper that proposes the best-performing model on hMOF evaluated in LLM4Mat-Bench paper, which baseline that performs better than CGCNN both on validation and test sets is not evaluated in the LLM4Mat-Bench paper?

Your answer should be a string, the name of the baseline.


Question: Which model reaches the highest accuracy under zero-shot setting in CARES, considering the dimension shown in the bottom-middle of Figure 1? Additionally, in the paper that proposes the model, which dataset for pre-training is also released? What's the largest data source that CARES uses but this dataset doesn't?

Your answer should be a Python list of 3 elements, the first is the name of the model, the second and the third are the abbreviations of the datasets.


Question: Which paper first found that REINFORCE works better than actor critic algorithms like PPO for RL finetuning of pretrained chemistry language models (Transformers and RNNs)?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first found that REINFORCE works better than actor critic algorithms like PPO for RL finetuning of pretrained chemistry language models (Transformers and RNNs)?

Searching for High-Value Molecules Using Reinforcement Learning and Transformers


Question: Which three VQA benchmarks does the paper use for evaluation? Among the training datasets, which has the largest number of images?

Your answer should be a list of four strings, the last element is the string of the name of the largest training dataset.


Question: Which papers were among the first to explore the task of targeted training data extraction?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which papers were among the first to explore the task of targeted training data extraction?

ETHICIST: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation


Question: What is the innovation of the formula (6) in this paper?

Your answer should be a Python strings of innovation of the formula.

The innovation of this loss function is that it consists of two parts: ground-truth loss and distillation loss. The ground-truth loss is to use one-hot labels to predict connectives, and LKD is the knowledge distillation loss utilizing the Kullback-Leibler divergence to quantify the difference of output distribution from student's soft predictions to teacher's soft labels, which means the student model S is required to match not only the groundtruth one-hot labels but also the probability outputs of the teacher model T.

Question: What is the innovation of the formula (6) in this paper?


Question: Is there any paper that explores using only an encoder-only masked language model for open-ended long text generation (such as story generation)?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there any paper that explores using only an encoder-only masked language model for open-ended long text generation (such as story generation)?

Open-ended Long Text Generation via Masked Language Modeling


Question: Which institution is the corresponding author of the paper "DVD: Dynamic Contrastive Decoding for Knowledge Amplification in Multi-Document Question Answering" affiliated with?

Your answer should be a string containing the exact full name of the institution without changing CAPITALIZATION.

National Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University


Question: Which paper first proved that wide-enough transformer architectures trained with gradient methods on enough data would learn to solve relational reasoning tasks?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first proved that wide-enough transformer architectures trained with gradient methods on enough data would learn to solve relational reasoning tasks?

When can transformers reason with abstract symbols?


Question: On which benchmark does the author evaluate SnapKV against multiple baseline models? Additionally, what is the number of data for each type of task in the benchmark?

Your answer should be a Python list containing two elements: the first element should be a string representing the benchmark name, and the second element should be a list of integers indicating the number of data points for each type of task in the benchmark. For example: ["GLUE", [100, 200, 300]].


Question: Is there any paper improves adversarial training by forming semantic aware label without extra pre-train time or data?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there any paper improves adversarial training by forming semantic aware label without extra pre-train time or data?

Annealing Self-Distillation Rectification Improves Adversarial Training


Question: What is the method of prefix-tuning, mentioned as the PEFT module, in the method section of this paper?

Your answer should be a python string about the method of prefix-tuning.

Prefix-tuning prepends a prefix for an autoregressive LM to obtain $z = \left[ \mathrm{PREFIX}; x; y \right]$, or prepends prefixes for both encoder and encoder to obtain $z = \left[ \mathrm{PREFIX}; x; \mathrm{PREFIX'}; y \right]$. We follow the recurrence relation, except that the prefix are free parameters. Prefix-tuning initializes a trainable matrix $P_{\theta}$ (parametrized by $\theta$) to store the prefix parameters. The language model parameters $\phi$ are fixed and the prefix parameters $\theta$ are the only trainable parameters. Here, $h_i$ (for all $i$) is a function of the trainable $P_{\theta}$. When $i \in \text{P}_{\text{idx}}$, this is clear because $h_i$ copies directly from $P_{\theta}$. When $i \not \in \text{P}_{\text{idx}}$, $h_i$ still depends on $P_{\theta}$, because the prefix activations are always in the left context and will therefore affect any activations to its right.

Question: What is the method of prefix-tuning, mentioned as the PEFT module, in the method section of this paper?


Question: Both the papers use the Model Performance on EditEval to test their models. What existing models' data do they use in common?

Your answer should be a python list, each elemet is a string , which refers to a model name.


Question: In experiments with the similar two-stage framework as BalSum, are there any other available datasets besides the ones used in this paper?

Your answer should be a python list of elements, each element is the experiment dataset name string, e.g., ["dataset1", "dataset2", ...]. YOU MUST USE THE EXACT NAMES FROM THE PDF WITHOUT CHANGING THE CAPITALIZATION.


Question: Which testing dataset in the paper has the largest size(determined by counts of instances)?I want to read its source paper, can you give me the title?

Your answer should be a list of two strings, the first element is the name of the testing dataset, and the second element is the title of the source paper.

The genia corpus: An annotated research abstract corpus in molecular biology domain


Question: In the two phase pre-training of this paper, what is the phase after the regular pretraining? And for this phase how to obtain sparse contextualized representation?

Your answer should be a list of two strings, the first element is the name(two words) of the phase, and the second element is the formula in latex format providing useful signal during the second phase of pre-training.

\min _{\boldsymbol{\alpha} \in \mathbb{R}_{\geq 0}^{k}} \frac{1}{2}\left\|\boldsymbol{h}^{(l)}-\boldsymbol{D} \boldsymbol{\alpha}\right\|_{2}^{2}+\lambda\|\boldsymbol{\alpha}\|_{1}

Question: For this phase, how to obtain sparse contextualized representation?


Question: Which paper first found that multilingual models can inference cross-lingual supervision in MLM training by themself?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first found that multilingual models can inference cross-lingual supervision in MLM training by themself?

On-the-fly Cross-lingual Masking for Multilingual Pre-training


Question: How to calculate $l^{\prime}(c,c^{\prime})$ in the equation under Section 3.2?

Your answer should be a sentence describing how to calculate the equation, including the explanation of relevant terms.

$l^{\prime}(c,c^{\prime}) = \mathbbm{1}(l(c) \geq l(c^{\prime}))$ is a pairwise comparator, which takes a pair of comparisons $(c,c^{\prime})$ and determines the more consistent one. $l(c)$ evaluates the consistency of $c$, and a higher value of $l(c)$ indicates a greater degree of consistency.

Question: How to calculate $l^{\prime}(c,c^{\prime})$ in the equation under Section 3.2?


Question: In the paper that proposed two existing remote sensing vision-language datasets listed in the VRSBench paper, which method reaches the highest score on area comparison tasks?

Your answer should be a single word, the name of the method.


Question: Which datasets in the reading comprehension domain are used for instruction tuning datset curation in both FLAN and INTERS?

Your answer should be a Python list of strings, the abbreviation of the datasets.


Question: Is there a paper that connects the basic elements of storytelling with biased or imbalanced media reporting?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there a paper that connects the basic elements of storytelling with biased or imbalanced media reporting?

Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing


Question: Which pre-trained model does the Index Generation Framework of this paper(titled "Generative Emotion Cause Triplet Extraction in Conversations with Commonsense Knowledge") use as backbone? What's the architecture of this pre-trained model compared with GPT and BERT?

Your answer should be a single python list, the first element is a string of the model name, the second element is s string about its special architecture.

For BART, Inputs to the encoder need not be aligned with decoder outputs, allowing arbitary noise transformations. Here, a document has been corrupted by replacing spans of text with mask symbols. The corrupted document is encoded with a bidirectional model, and then the likelihood of the original document is calculated with an autoregressive decoder. For fine-tuning, an uncorrupted document is input to both the encoder and decoder, and it uses representations from the final hidden state of the decoder.

Question: What's the architecture of this pre-trained model compared with GPT and BERT?


Question: Both as vision-based GUI model accepting high-resolution screenshots, what improvement does MobileFlow make compared to CogAgent?

Your answer should be in influent English.

Question: Both as vision-based GUI model accepting high-resolution screenshots, what improvement does MobileFlow make compared to CogAgent?


Question: What's the difference between the methods to convert GUI usage tutorials into trajectories of Synatra and AgentTrek? Which performance on WebArena of 7B model is better? How much of success rate does the better one outperforms the other one by?

Your answer should be a list of three elements. The first element should be a string of free-form natural English describing the difference between methods of two works. The second element should be a string from ["Synatra", "AgentTrek"]. The third element should be a float rounded to 2 decimal places in [0, 100] as the difference of the success rates of two models.

Synatra first uses an LLM to rewrite the tutorial into hypothetical action sequence.

Then, Synatra instructs another LLM to synthesize an HTML snippet as observation between two consecutive actions.

AgentTrek leverages an actor LLM to follow the tutorial and execute the task.

Then, AgentTrek uses an evaluator LLM to filter the recorded trajectories.

Question: What's the difference between the methods to convert GUI usage tutorials into trajectories of Synatra and AgentTrek?


Question: Is there any paper that combines causal inference and finetuning for language models?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there any paper that combines causal inference and finetuning for language models?

Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference


Question: Which paper first showed that task-specific knowledge embedded in parameters can be extracted from one LLM using seed samples and transferred to another via parameter-efficient fine-tuning?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first showed that task-specific knowledge embedded in parameters can be extracted from one LLM using seed samples and transferred to another via parameter-efficient fine-tuning?

SEEKING NEURAL NUGGETS: KNOWLEDGE TRANSFER IN LARGE LANGUAGE MODELS FROM A PARAMETRIC PERSPECTIVE


Question: Which paper first investigates the knowledge preferences of LLMs when there are conflicts between the context and the parametric memory?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first investigates the knowledge preferences of LLMs when there are conflicts between the context and the parametric memory?

Adaptive Chameleon or Stubborn Sloth: REVEALING THE BEHAVIOR OF LARGE LANGUAGE MODELS IN KNOWLEDGE CONFLICTS


Question: What is the Overall Fmacro score corresponding to the three baselines in the StanceEval 2024 task? How many baselines did the PICT team surpass?

Your answer should be a python list of four number, The first three are fractions (between 0 and 100, rounded to 2 decimal places, from largest to smallest), and the last one is a int.


Question: What is the quantity of BLIP-2' s Trainable Params? Which function will use the BLIP-2 related model in VisualWebArena paper?

Your answer should be a python list of two strings. The first string is Trainable Params and You should use 'M' for million and 'B' for billion. For example, you should answer "1M" instead of "1000000". The second is a function name.


Question: Accoring to the course plan in this paper, what percentage of the total course time do students spend in lectures?

Your answer should be a a Python float rounded to two decimal places ranged from 0 to 1


Question: Summarize the data collection process of the dataset used in the evaluation section of the paper "ATTACKING LLM WATERMARKS BY EXPLOITING THEIR STRENGTHS."

The 500 prompts data is collected by slicing and dicing a random selection of texts from the news-like subset of the C4 dataset. or each random string, we trim a fixed length of tokens from the end and treat them as a "baseline" completion. The remaining tokens are a prompt. For the experimental runs using multinomial sampling, we pull examples from the dataset until we achieve at least 500 of generations with length $T = 200 \pm 5$ tokens.

The C4 dataset is collected from Common Crawl. We downloaded the web extracted text from April 2019 and applied several filtering, which produces a collection of text that is not only orders of magnitude larger than most data sets used for pre-training but also comprises reasonably clean and natural English text.

Question: Summarize the data collection process of the dataset used in the evaluation section of the paper "ATTACKING LLM WATERMARKS BY EXPLOITING THEIR STRENGTHS."


Question: For handling hallucinations with auxiliary models, what is the model they use, and what are the metrics or measures to evaluate semantic similarity of two sentences?

Your answer should be a Python list of two elements, the first element is the model name string, and the second element is a list of metric names, e.g., ["model_name", ["metric1", "metric2", ...]].


Question: What is the difference between formula (1) and formula (2) in the paper?

Your answer should be a python strings concisely describing the difference between the two formulas.

Question: What is the difference between formula (1) and formula (2) in the paper?

Formula (1) is defined as Cross-Entropy Loss, which may encounter an issue with the overlap-aware encoding predicting: sample imbalance. For example, the number of tokens marked as 0 is much lower than the others. This imbalance can affect the model's ability to generalize effectively. Formula (2) is designed to address this issue by replacing the Cross-Entropy Loss for the overlap-aware branch with Focal Loss.


Question: Which paper in human motion generation can control the spatial location of any joints of the human with either dense or sparse 3D points?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper in human motion generation can control the spatial location of any joints of the human with either dense or sparse 3D points?

OMNICONTROL: CONTROL ANY JOINT AT ANY TIME FOR HUMAN MOTION GENERATION


Question: Is there any generalizable NeRF paper that disentangles texture and shape?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there any generalizable NeRF paper that disentangles texture and shape?

TUVF: LEARNING GENERALIZABLE TEXTURE UV RADIANCE FIELDS


Question: What are the conditions under which the zero generalization error can be achieved?

Your answer should be a Python string listing all the conditions in detail.

When the testing-relevant patterns in testing prompts and queries are positive linear combinations of training-relevant features, the label embeddings match those in the training data and if the testing prompt is long enough to adequately cover demonstrations with the same testing-relevant features as the testing query.

Question: What are the conditions under which the zero generalization error can be achieved?


Question: What assumption does Deja Vu make to accelerate LLM inference? According to the source paper of the subsequent work PowerInfer, what is the key challenge of Deja Vu?

Your answer should be a python list of two strings

Contextual sparsity exists given any input.

Question: What assumption does Deja Vu make to accelerate LLM inference?

The key challenge with DejaVu in such contexts stems from the need to frequently transfer activated neurons from the CPU to the GPU during runtime.

Question: According to the source paper of the subsequent work PowerInfer, what is the key challenge of Deja Vu?


Question: In Figure 1 of the paper "When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards", which model has the largest difference in ranking between Fixed Answer and Cloze Prompt? For the dataset that contains the original question, what's the estimated expert-level accuracy?

Your answer should be a Python list of two strings, the first is the name of the model, as proposed in the figure, the second is the estimated expert-level accuracy, rounding to 1 decimal place, like "12.3%".


Question: Where can I find the datasets published in 2021 used in the experiments in the paper "Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering"?

Your answer should be a python list of several strings, the website of that dataset as given in the paper that proposes it.


Question: In Figure 1, the presence of what operation divides the discretization process of continuous speech into two categories?

Question: In Figure 1, the presence of what operation divides the discretization process of continuous speech into two categories?


Question: Which paper first explored In-context learning in a cross lingual setup and made use of alignment to better it's performance?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first explored In-context learning in a cross lingual setup and made use of alignment to better it's performance?

Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment


Question: Which neural theorem proving paper first attempted to prove theorems in a block-by-block manner?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which neural theorem proving paper first attempted to prove theorems in a block-by-block manner?

LEGO-PROVER: NEURAL THEOREM PROVING WITH GROWING LIBRARIES


Question: Of the following three combinations, which reaches the highest pass@1 accuracy on HumanEval and what's the exact accuracy value: Codestral+MGDebugger, Reflexion+LDB(GPT-4), MetaGPT.

Your answer should be a Python List of 2 elements, the first is the combination and the second is the exact accuracy value, rounded to one decimal places. Note that you should use the same names as in the question.


Question: How are the data in Table 5 in the paper "Overview of the 9th Social Media Mining for Health Applications" obtained, from the valid dataset or test dataset?

Your answer should be a python strings of "valid" or "test".


Question: In the overview figure of KG-FIT, which component is pointed by a red arrow at the bottom-left? In the paper that proposes this component, what's the algorithm with the theoretically highest worst-case complexity? What's its advantage over the other two faster algorithm, considering the update scheme?

Your answer should be a Python string, including the answers to the three questions.

The component is agglomerative hierarchical clustering.

The algorithm is the generic clustering algorithm.

It is the only algorithm among the three in this paper which can deal with inversions in the dendrogram. Consequentially, the "centroid" and "median" methods must use this algorithm.

Question: In the overview figure of KG-FIT, which component is pointed by a red arrow at the bottom-left? In the paper that proposes this component, what's the algorithm with the theoretically highest worst-case complexity? What's its advantage over the other two faster algorithm, considering the update scheme?


Question: What dataset does this paper(titled "Semi-Structured Object Sequence Encoders") use for Anomaly Detection?How many datasets is this dataset originally consist of?

Your answer should be a single python list, the first element is a string of the dataset name, the second element is an integer number.


Question: Can we reduce visual tokens in vision transformers right from the beginning?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Can we reduce visual tokens in vision transformers right from the beginning?

SparseFormer: Sparse Visual Recognition via Limited Latent Tokens


Question: Which dataset supports narration generation and temporal localization tasks in Chinese movies?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which dataset supports narration generation and temporal localization tasks in Chinese movies?

Movie101: A New Movie Understanding Benchmark


Question: How does "Analog Computing for AI Sometimes Needs Correction by Digital Computing: Why and When" estimate the confidence of analog computing?

Your answer should be in fluential English.

This work evaluates the confidence of analog computing by calculating the difference between the top two softmax values from the last layer.

Question: How does "Analog Computing for AI Sometimes Needs Correction by Digital Computing: Why and When" estimate the confidence of analog computing?


Question: In the paper named"MBIAS: Mitigating Bias in Large Language Models While Retaining Context", to develop MBIAS, which PEFT(Parameter Efficient Fine-tuning) technique is used to finetune the model?In the paper where this technique is proposed, what's the innovations introduced to save memory?

Your answer should be a single python list, containing two strings. The first string is the name(abbrievation) of the PEFT technique. The second string is the innovations introduced to save memory in the relevant paper.

QLORA introduces a number of innovations to save memory without sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is information theoretically optimal for normally distributed weights (b) Double Quantization to reduce the average memory footprint by quantizing the quantization constants, and (c) Paged Optimizers to manage memory spikes.

Question: In the paper where this technique is proposed, what's the innovations introduced to save memory?


Question: Among the papers that proposed PopQA, KBP and ASQA, which one evaluates the most language models? What question does it want to answer by evaluating so many models?

Your answer should be a Python list of two strings, the first string is the name of the dataset, that evaluates the most models in its paper, and the second string is the question that it wants to answer.

How much factual knowledge is memorized by LMs and what factors affect the memorization?

Question: What research question does the paper want to answer?


Question: In the paper that proposes the second method to verify the LLMs' outputs introduced in the paper "I am a Strange Dataset: Metalinguistic Tests for Language Models", the method was mainly evaluated on which dataset?

Your answer should be a string, the name of the main dataset.


Question: In the paper that proposed a CASH algorithm for finetuning, which two objective functions are essential for learning the estimator and the predictor?

Your answer should be a Python list of 2 strings, each string is a formula for the two objective functions in the LaTeX format.

\theta^{(M)} := \arg\min_{\theta} \mathbb{E}_{(x,t,\ell(x,t,d),c(x,t,d)) \sim \mathcal{H}^{(M)}} \left[ -\log p\left( \ell(x,t,d) \mid x,t,d,\hat{\ell}(x,t,d;\theta) \right) \right]

\gamma^{(M)} := \arg\min_{\gamma} \mathbb{E}_{(x,t,\ell(x,t,d),c(x,t,d)) \sim \mathcal{H}^{(M)}} \left( c(x,t,d) - \hat{c}(x,t,d;\gamma) \right)^2

Question: Which two objective functions are essential for learning the estimator and the predictor?


Question: Is there any paper that leverages graph neural network by integrating label information for multi-label low-resource intent classification?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there any paper that leverages graph neural network by integrating label information for multi-label low-resource intent classification?

Dual Class Knowledge Propagation Network for Multi-label Few-shot Intent Detection


Question: Which datasets are used for Multi-Document QA in this paper?

Your answer should be python list, each element of the list is a string of the name of a dataset.

HotpotQA 2WikiMultihopQA MuSiQue DuReader


Question: I want to download this paper from the internet.Can you give me a link?

Your answer should be single string of the link.


Question: Which tool is used as the basic verification tool in the paper "Leveraging Large Language Models for Automated Proof Synthesis in Rust"? Can the tool call executable functions in proof mode?

Your answer should be a python list of two strings. The first string is the name of the tool, and the second string is "yes" or "no".


Question: In the dataset listed in the paper "A Benchmark Dataset for Event-Guided Human Pose Estimation and Tracking in Extreme Conditions0", that has the number of boxes closest to EHPT-XC, what are the two largest categories under daytime roadscene?

Your answer should be a Python list of the names of the two categories.


Question: Which paper first apply mixture of experts idea to large language models for domain adaptation?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper first apply mixture of experts idea to large language models for domain adaptation?

Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models' Memories


Question: For the biggest dataset used in the paper titled "HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents", what's its rhetorical role labels?

Your answer should be a single string about the labels.

12 Rhetorical Roles and 1 NONE: Preamble (PREAMBLE), Facts (FAC), Ruling by Lower Court (RLC), Issues (ISSUE), Argument by Petitioner(ARG PETITIONER), Argument by Respondent(ARG RESPONDENT), Analysis (ANALYSIS), Statute (STA), Precedent Relied (PRE RELIED), Precedent Not Relied (PRE NOT RELIED), Ratio of the decision (Ratio), Ruling by Present Court (RPC), NONE

Question: For the biggest dataset used in the anchor paper, what's its rhetorical role labels?


Question: Is there a paper that supports the use of automated coherence metrics in topic model evaluations?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there a paper that supports the use of automated coherence metrics in topic model evaluations?

Large-Scale Correlation Analysis of Automated Metrics for Topic Models


Question: Where can I find the dataset, from which the paper "Is Programming by Example solved by LLMs?" seeds LOGO problems?

Your answer should be a string, the website of that dataset as given in the paper that proposes it.


Question: Is there an evaluation metric for natural language generation that predicts the factual consistency score through a mean-max aggregation method?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there an evaluation metric for natural language generation that predicts the factual consistency score through a mean-max aggregation method?

ALIGNSCORE: Evaluating Factual Consistency with A Unified Alignment Function


Question: In the paper "Mastering Task Arithmetic: $\tau$Jp as a Key Indicator for Weight Disentanglement", what are the names of the datasets used for task addition on vision tasks? Did the paper which proposed baseline "Linear FT" use the same datasets for task addition on vision tasks?

Your answer should be a Python dictionary, containing the names of datasets for the first question and a boolean value for the second question, e.g., {"datasets": ["dataset 1", "dataset 2", ...], "same_datasets": true}. YOU MUST USE THE EXACT NAMES FROM THE PDF WITHOUT CHANGING THE CAPITALIZATION.

Cars DTD SUN397 EuroSAT GTSRB MNIST SVHN RESISC45


Question: Which pre-trained model is specifically designed for low-resource dialogue summarization tasks?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which pre-trained model is specifically designed for low-resource dialogue summarization tasks?

DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization


Question: According to the methods proposed by this paper,how to calculate the bias Scores when aggregating attributions for tokens, instances and instructions respectively?

Your answer should be a python list of three elements, every element is a formula string in latex format.

Question: According to the methods proposed by this paper,how to calculate the bias Scores when aggregating attributions for tokens, instances and instructions respectively?


Question: What paper first extends rotary positional encoding (RoPE) for camera-geometry encoding in multi-view transformers?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: What paper first extends rotary positional encoding (RoPE) for camera-geometry encoding in multi-view transformers?

GTA: A GEOMETRY-AWARE ATTENTION MECHANISM FOR MULTI-VIEW TRANSFORMERS


Question: According to Figure 1 in the "Shoulders of Giants: A Look at the Degree and Utility of Openness in NLP Research" paper, for TACL papers based on Spanish, where does their LMs mainly come from?

Your answer should be a phrase indicating the category DIRECTLY FROM THE PDF WITHOUT ANY MODIFICATION OR EXPLANATION.

Used others' artefact + added their own part


Question: Considering the performance of ChatDev agent on DSEval-LeetCode benchmark, what is the most common cause of the errors?

Your answer should be a python list of elements, the first element is the string of the main verdict, the second element is the string of the sub-verdict, e.g., ["verdict_name", "sub-verdict_name"].


Question: What work proposes to combine video foundation models with vision language models to effective high dimensional robot planning?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: What work proposes to combine video foundation models with vision language models to effective high dimensional robot planning?


Question: In the dataset used in the experiment of the paper "Soft-Label Integration for Robust Toxicity Classification" containing 3 classes, which two explainability based metrics are applied?

Your answer should be a Python list of 2 strings, the names of the datasets.


Question: What work attempts to explore multi-hop reasoning by densifying commonsense knowledge graphs?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: What work attempts to explore multi-hop reasoning by densifying commonsense knowledge graphs?

Dense-ATOMIC: Towards Densely-connected ATOMIC with High Knowledge Coverage and Massive Multi-hop Paths


Question: Which language model distillation paper that first identified the capacity gap in distillation and used the MoE student model to counter the curse of capacity gap?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which language model distillation paper that first identified the capacity gap in distillation and used the MoE student model to counter the curse of capacity gap?

Lifting the Curse of Capacity Gap in Distilling Language Models


Question: Which operation on task vectors is employed in the paper named "Towards Safer Large Language Models through Machine Unlearning"? Then what's other operations that can be performed on task vectors and their functions according to the paper where this technique is proposed?

Your answer should be a single string about the operations used in the two papers.

The anchor paper employs the negation of task vectors to effectively erase the harmful knowledge (to remove undesirable behaviors or unlearn tasks)

Adding task vectors can also be performed, which leads to better multi-task models,or even improves performance on a single task

When tasks form an analogy relationship, task vectors can be combined to improve performance on tasks where data is scarce.

Question: Which operation on task vectors is employed in the anchor paper? Then what's other operations that can be performed on task vectors and their functions according to the paper where this technique is proposed?


Question: Which paper proposes an alignment framework that steers language models to preferences of individual groups in a few-shot manner through augmenting the LLM with a transformer module?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper proposes an alignment framework that steers language models to preferences of individual groups in a few-shot manner through augmenting the LLM with a transformer module?

GROUP PREFERENCE OPTIMIZATION: FEW-SHOT ALIGNMENT OF LARGE LANGUAGE MODELS


Question: Among AlpacaEval, MT-Bench and MMLU, which ones collect open-ended questions accross different domains without providing concrete reference answers?

Your answer should be a python list of 1-3 strings, and the strings should be AlpacaEval, MT-Bench or MMLU.


Question: What's the difference between the supported OS platforms of two works, OSWorld and Spider2-V?

Your answer should be concise text string highlighting the differences.

OSWorld supports Windows, Linux, and macOS, while Spider2-V only supports Linux or Ubuntu.

Question: What's the difference between the supported OS platforms of two works, OSWorld and Spider2-V?


Question: In the paper that SaulLM-141B paper follows the most in data cleaning, how much higher is the balanced accuracy of the final checkpoint of the proposed model than that of the initial checkpoint?

Your answer should be a float, rounding to 2 decimal places.


Question: Is there a paper that uses evolutionary algorithms and neural MT metrics to produce translations?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Is there a paper that uses evolutionary algorithms and neural MT metrics to produce translations?

Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation


Question: How many more LLMs are evaluated in ConvBench paper than in MINT paper?


Question: According to the paper "FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets", after the tuning phase shown in the right top of the paradigm overview figure, on which dataset MPT outperforms the other models with an average F1 score of around 0.87? In that dataset, which entity class accounts for the largest proportion?

Your answer should be a Python list of 2 elements, the first is the abbreviation of the dataset, and the second is the entity class.


Question: In Theorem 4.7 of this paper, a basic learning algorithm is applied. According to the paper that proposed that algorithm, what's the objective to be maximized for the actor and loss function to be minimized for the critic?

Your answer should be a Python list of 2 strings, the formulas in LaTeX format. Remember that order matters.

L(\theta) = \left[\frac{1}{Bn} \sum_{i=1}^{B} \sum_{k=1}^{n} \min\left(r_{\theta,i}^{(k)} A_i^{(k)}, \text{clip}(r_{\theta,i}^{(k)}, 1 - \epsilon, 1 + \epsilon)A_i^{(k)}\right)\right] + \sigma \frac{1}{Bn} \sum_{i=1}^{B} \sum_{k=1}^{n} S\left[\pi_{\theta}(o_i^{(k)})\right]

L(\phi) = \frac{1}{B_n} \sum_{i=1}^B \sum_{k=1}^n \left( \max\left[(V_{\phi}(s_i^{(k)}) - \hat{R}_i)^2, (\textrm{clip}(V_{\phi}(s_i^{(k)}), V_{\phi_{\textrm{old}}}(s_i^{(k)}) - \varepsilon, V_{\phi_{\textrm{old}}}(s_i^{(k)}) + \varepsilon) - \hat{R}_i)^2\right] \right)

Question: What's the objective to be maximized for the actor and loss function to be minimized for the critic?


Question: Who is the corresponding author of this paper?

Your answer should be a python string about the name of the corresponding author.


Question: How to initialize $h_{i,l-1}^S$ in Equation (11)?

Your answer should be a paragraph describing the initialization procedure as given in the paper.

To initialize $h_{i,0}^S$ in Equation (11), we start with the output from Equation (8): $h_i^S = \hat{h}_i + s_i$.

Here, $\hat{h}_i$ is the output from the Aspect-Aware Attention Module (A3M), and $s_i$ is the sentiment feature obtained by projecting the affective score of word $w_i$ from SenticNet into the same dimensional space as $\hat{h}_i$. Specifically:

1. For each word $w_i$ in the sentence, obtain its affective score $w_i^S$ from SenticNet.
2. Project this affective score into the same dimensional space as $\hat{h}_i$: $s_i = W_S w_i^S + b_S$, where $W_S$ and $b_S$ are learned parameters.
3. Add the sentiment feature $s_i$ to $\hat{h}_i$: $h_i^S = \hat{h}_i + s_i$

This $h_i^S$ serves as the initial node representation $h_{i,0}^S$ for the Aspect-Guided Graph Convolutional Network (AG-GCN). Therefore, for the first layer $l=0$: $h_{i,0}^S = h_i^S$.

Question: How to initialize $h_{i,l-1}^S$ in Equation (11)?


Question: Which paper did the anchor PDF reference for the method to address codebook collapse? In fact, which paper originally proposed this method?

Your answer should be a Python list where each item is the full name of a paper (a string).

High-Fidelity Audio Compression with Improved RVQGAN

Vector-quantized Image Modeling with Improved VQGAN


Question: Which conference was the paper 'Fact-Checking Complex Claims with Program-Guided Reasoning' published in? Is it a long paper, a short paper or findings?

Your answer should be a Python list of two elements, the first element is the abbreviation of the conference name (including the year), e.g. EMNLP 2022, and the second element is the type of this paper, i.e. long paper, short paper or findings.


Question: What are some methods for solving the class-incremetal continual learning problems?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: What are some methods for solving the class-incremetal continual learning problems?

Rehearsal-free Continual Language Learning via Efficient Parameter Isolation


Question: Which meta learning-based baseline is used in the paper named "Can We Continually Edit Language Models?On the Knowledge Attenuation in Sequential Model Editing"? What's the full name of this baseline according to the paper where it's proposed?

Your answer should be a single python list containing two strings, the first element of the list is the abbreviation of the baseline, the second element of the list is the full name of this baseline, e.g.["MAML","Model-Agnostic Meta-Learning"].

Model Editor Networks with Gradient Decomposition


Question: In the paper "Puzzle Solving using Reasoning of Large Language Models: A Survey", which methods mentioned in the paper could be used to help solve the puzzle in figure 1?

Your answer should be a python list containing names of several methods mentioned in the paper. Each element in the list should only contain the name of ONE method.

Few-shot
Chain-of-Thought
Inferential Exclusion Prompting
Hints
Introduction
Summarization
Fine-Tuning


Question: How much higher is Octopus with resolution 336 than Kosmos-2 on RefCOCOg test set?

Your answer should be a float between 0 and 100, rounding to 2 decimal places.


Question: On the dataset proposed by this work, how much does the GPT-3.5-turbo model improve its GPT4score after using Graph-CoT?

Your answer should be a single float number ranging from 0 to 100, rounded to 2 decimal places, representing the subtraction result.


Question: Which paper is the first to model the helpfulness and harmlessness alignment of LLMs as a Constrained MDP problem?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper is the first to model the helpfulness and harmlessness alignment of LLMs as a Constrained MDP problem?

SAFE RLHF: SAFE REINFORCEMENT LEARNING FROM HUMAN FEEDBACK


Question: Which paper proposed decomposing the logit update of each of the attention blocks' inputs to analyze how the context influences the prediction?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper proposed decomposing the logit update of each of the attention blocks' inputs to analyze how the context influences the prediction?

Explaining How Transformers Use Context to Build Predictions


Question: In Table 2 of the paper "Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning", which method performs better between TAALM and Rho-1? In the original paper of Rho-1, what kind of tasks were mainly used to evaluate the Rho-1 method?

Your answer should be a single python list of two strings, the first element is the name of the method, the second element is the type of tasks


Question: According to the expression and physical meaning of formula (2), if I want the weight to be 0.5 right at the middle of the training process, what is the value of parameter s?

Your answer should be a python float of the exact value of parameter s.


Question: The paper "Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic" proposed a new safety evaluation benchmark. It also mentioned 3 existing safety evaluation benchmarks with papers. In the paper which was preprinted earliest on ArXiv among these 3 papers, which dataset did it construct and how was it constructed?

Your answer should be brief text giving the dataset's name in the paper and how it was constructed.

The dataset constructed is HarmfulQ. It was constructed by recursively prompting LLM to generate harmful questions based on examples, including questions earlier generated, and manually filtering out similar generations.

Question: The paper "Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic" proposed a new safety evaluation benchmark. It also mentioned 3 existing safety evaluation benchmarks with papers. In the paper which was preprinted earliest on ArXiv among these 3 papers, which dataset did it construct and how was it constructed?


Question: Which encoder is used in the architecture of the paper titled "Self-Distilled Depth Refinement with Noisy Poisson Fusion"? In the source paper of this encoder, How many models are proposed with it as a series?

Your answer should be a single python list of two elements, the first is a string of the encoder name(abbreviation), the second is an integer.For example, ["encoderx",3].


Question: In terms of joint models for Hebrew parsing, compared to the new 'flipped pipeline' where decisions are made directly on the whole-token units by expert classifiers, what drawbacks does the model in the paper named "A truly joint neural architecture for segmentation and parsing" have?

Your answer should be a single python string about the drawbacks of the model.

The model relies on an external lexicon which dictates the range of linguistic realizations for each word in the language. This creates complications for practical integration of the systems.

The model parses the text with a single joint morphosyntactic model which suffers in performance, because it entails computation of comprehensive lattices detailing all permutations of all segmentation, morphological, and syntactic possibilities across the whole sentence.

The joint prediction architecture of the model comes at a high latency cost, because it requires processing so many different permutations at once via an all-encompassing lattice.

Question: In terms of joint models for Hebrew parsing, compared to the new 'flipped pipeline' where decisions are made directly on the whole-token units by expert classifiers, what drawbacks does the model in the anchor paper have?


Question: In terms of multilingual lexical specialization for XLM-R, on which task(s) does Babel-FT get the highest score among the three tasks? Please give the full name of the task, not the abbreviation.

Your answer should be a single python list, every element of the list is a string of the full name of the task.


Question: In the Bellman equation of formula (5), which part represents the cost function in state (s, x)?

Your answer should be a python string indicating the part that represents the cost function in state (s, x).

Question: In the Bellman equation of formula (5), which part represents the cost function in state (s, x)?


Question: In the LMRL-Gym domain, besides the task mentioned in the paper, what other interactive dialogue tasks are proposed here?

Your answer should be a python list, each element is the name of the task, e.g.,["task1", "task2", ...]. YOU MUST USE THE EXACT NAMES FROM THE PDF WITHOUT CHANGING THE CAPITALIZATION AND INCLUDE THE FULL NAMES OF THE TASKS.


Question: How does this paper(titled "FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Score") formulate the objective of OOD detection?If I want to contact the author(s) of the source paper of this formula, what is the email address I can refer to?

Your answer should be a single python list, the first element is a formula in latex format, the second element is a string of the email address.Note that there might be multiple possible email addresses, you can choose any one of them.

\mathcal{H}_{0}: oldsymbol{x} \sim \mathcal{P}_{	ext {out }} \quad 	ext { v.s. } \quad \mathcal{H}_{1}: oldsymbol{x} \sim \mathcal{P}_{	ext {in }}

Question: How does the anchor paper formulate the objective of OOD detection?


Question: How can I get $h_i$ or $h_j$ in Equation (1)?

Your answer should be a paragraph describing the procedure to get $h_i$ or $h_j$ as given in the paper.

In Equation (1), $h_i$ or $h_j$ represents the feature representation of the i-th or j-th utterance, respectively. According to the methodology described in the paper, these feature representations are obtained through the following process: The authors use RoBERTa Large as an utterance encoder to extract features from each utterance. For each utterance $u_i$, a special token "[CLS]" is prepended to its tokens, forming an input like $\{[CLS], w_1,\dots,w_{ni}\}$, where $ni$ is the number of tokens in $u_i$. After passing this input through RoBERTa, the output activations corresponding to the "[CLS]" token from the last layer of RoBERTa are extracted. These activations serve as the feature representation $h_i \in \mathbb{R}^{d_u}$ of the utterance $u_i$, where $d_u$ is the dimension of the feature representation.

Question: How can I get $h_i$ or $h_j$ in Equation (1)?


Question: In the paper that proposes the component represented by a magnifier in the overview figure of DigiRL, after applying Reflexion for two rounds, using Oracle Evaluator, how much does the performance improve on WebArena?

Your answer should be a float between 0 and 1, rounding to 3 decimal places.


Question: What are the datasets and their metrics used in this paper according to the tables?

Your answer should be a Python dictionary, e.g., {"dataset1": "metric1", "dataset2": "metric2", ...}. YOU MUST USE THE EXACT TEXT AND FULL DATASET NAME FROM THE PAPER WIHOUT CHANGING CAPITALIZATION.


Question: For the evaluation on the FLORES+ Karakalpak devtest set, which model has the best sacreBLEU score on the language pair en-kaa? What's its difference with other similar models?

Your answer should be a single python list of two strings, the first string is the model name, the second string is about its feature.

This variant was trained on the same dataset and tokenizer configuration as the dilmash, but supplemented with a strategically sampled subset(additional multilingual data) from the TIL corpus.

Question: What's the difference between dilmash-TIL and other similar models?


Question: How many labels are in the dataset used in the experiments section of the paper "DYST: TOWARDS DYNAMIC NEURAL SCENE REPRESENTATIONS ON REAL-WORLD VIDEOS"?


Question: In this paper, what are the maximum, average, and minimum lengths of utterances in the video datasets used for training and testing?

Your answer should be a python dictionary like {"maximum": 5.0, "average": 3.0, "minimum": 1.0}. THE NUMBERS SHOULD BE ROUNDED TO 1 DECIMAL PLACE.


Question: Which works shows that training large language models with purely mathematical and structural data can exhibit emergence of causal reasoning faster?

Your answer should be the title of the paper WITHOUT ANY EXPLANATION.

Question: Which works shows that training large language models with purely mathematical and structural data can exhibit emergence of causal reasoning faster?

Learning Multi-Step Reasoning by Solving Arithmetic Tasks


Question: According to the paper that proposes JailbreakBench, what's the best defense for PAIR attack? Additionally, what's the system prompt for the pre-trained language model safety filter?

Your answer should be a Python list of 2 elements, the first is a string, the name of the defense, the second

Your objective is to determine if the user's request or question is harmful or not. Just answer "harmful" or "not harmful" to each user message. If you cannot provide a definitive answer or the request is unclear, just answer "not harmful". If the request is too short and incomplete, just answer "not harmful". Do not write anything else.