Question: What is the average proportion of papers in the ACL anthology in recent ten years which mention the words speech, spoken or audio in the title?

Your answer should be a Python float number roudning to 3 decimal places, e.g., 0.001.


Question: Which two prompting methods in the two papers have similar principles?

Your answer should be a python list of two prompting method. You must use abbreviations as the papers given.


Question: When conducting experiments, which kind of GPU device is used in this paper?

All experiments were done with an Nvidia A40 GPU in this paper.

Question: When conducting experiments, which kind of GPU device is used in this paper?


Question: Has any study explored the zero-shot extraction of persona characteristics within conversational dialogues?

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

Question: Has any study explored the zero-shot extraction of persona characteristics within conversational dialogues?

PAED: Zero-Shot Persona Attribute Extraction in Dialogues


Question: Which paper first tried to fine-tune LLMs with chain-of-thoughts and program-of-thoughts for math reasoning?

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

Question: Which paper first tried to fine-tune LLMs with chain-of-thoughts and program-of-thoughts for math reasoning?

MAMMOTH: BUILDING MATH GENERALIST MODELS THROUGH HYBRID INSTRUCTION TUNING


Question: How many multi-modal baselines excluding the method this paper proposed do authors use? Among this baselines, who reaches the highest F1 score on Twitter2015 dataset? And what about Twitter2017 dataset?

Your answer should be a python list, the first item is the number of multi-modal baselines the paper used, the second item and the third item are the name of methods reaching the highest F1 score on Twitter2015 and Twitter2017 dataset respectively.


Question: Is there a paper which applies Bayesian optimization to modular continual learning?

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

Question: Is there a paper which applies Bayesian optimization to modular continual learning?

A Probabilistic Framework for Modular Continual Learning


Question: For Zero-shot performance on unseen languages, which model in the experiment of the paper(titled "Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages") gets the highest F1 score? In the source paper of this model, which languages is it evaluated on?

Your answer should be a python list like ["string1", ["string2", "string3", ...]]. The first element should be a string, representing the name of the model. The second element should be a list of strings, representing the languages.NOTE that the languages should be in the format of ISO 639-3 code.


Question: Which paper first construct large-scale corpus to improve in-context learning of large language models in the pre-training stage?

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

Question: Which paper first construct large-scale corpus to improve in-context learning of large language models in the pre-training stage?


Question: How many more layers of Transformer should the new method compute compared with the standard LLM in a LLM with 13B parameters?


Question: What training acceleration methods are compared in the original paper that describes the methods used in training the FLM-101B model?

Your answer should be a python list of strings.

Stacking CompoundGrow Staged Bert2BERT LiGO


Question: What are some data-efficient ways to learn text embeddings thru contrastive learning?

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

Question: What are some data-efficient ways to learn text embeddings thru contrastive learning?

Composition-contrastive Learning for Sentence Embeddings


Question: From which two subsets does the benchmark used as evaluation set for BLOOM and BLOOMZ models in the paper merged?(The paper is named "An Empirical Study of In-context Learning in LLMs for Machine Translation")

Your answer should be a single python list, every element of the list is a string of the abbrievation name of the subset, e.g.["TAT-Conv","TAT-Web"].


Question: Is there a theory paper that explains why sometimes tuning momentum does not boost performance for training a neural network?

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

Question: Is there a theory paper that explains why sometimes tuning momentum does not boost performance for training a neural network?

The Marginal Value of Momentum for Small Learning Rate SGD


Question: Which approach was first proposed to solve the text-conditioned image retrieval task according to the SDA paper? In this approach, how to compute gating connection?

Your answer should be a python list of two strings, the first is an approach name and you must use abbreviation as given in the papers. The second is a fomula in latex format.

f_{\text{gate}}(\phi_x, \phi_t) = \sigma \left( W_{g2} * \text{RELU} \left( W_{g1} * [\phi_x, \phi_t] \right) \right) \circ \phi_x

Question: How to compute gating connection?


Question: In the PRL paper, besides the proposed method, which baseline performs better? In the paper that proposes that baseline, what's the loss function for the controller?

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

Question: What's the loss function for the controller?


Question: According to Figure 1, in which year has the highest proportion of NLP papers that explicitly mention speech-related terms in their title?

Your answer should be the year number with the highest proportion, e.g. 2000.


Question: Is there any paper that uses Lipschitz continuity in learning a dynamics model?

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

Question: Is there any paper that uses Lipschitz continuity in learning a dynamics model?

CCIL: CONTINUITY-BASED DATA AUGMENTATION FOR CORRECTIVE IMITATION LEARNING


Question: What paper provides generalization bounds for self supervised learning models eg. CLIP

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

Question: What paper provides generalization bounds for self supervised learning models eg. CLIP

Understanding prompt engineering may not require rethinking generalization


Question: Which baselines are chosen to study for Seperate Training based methods in the paper named "Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation"? In the source paper of the baseline 'Prior', what control framework is proposed?

Your answer should be a single python list,the first element is a list of strings of the baselines, the second element is the string about the control framework.e.g.[["Prior","Baseline2"],"The paper proposes a novel control framework that introduces..."].

The paper proposes a novel framework that introduces a well-formed prior space for effective and flexible control via invertible transformation.

Question: For the source paper of the baseline 'Prior', what control framework is proposed?


Question: Which paper produces a dataset for text simplification in over 12 languages and evaluates both finetuning and in context learning approaches to text simplification in those languages?

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

Question: Which paper produces a dataset for text simplification in over 12 languages and evaluates both finetuning and in context learning approaches to text simplification in those languages?

Revisiting non-English Text Simplification: A Unified Multilingual Benchmark


Question: Is there any paper that automatically creates a dataset for summarizing text from one language to another for a large collection of languages?

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

Question: Is there any paper that automatically creates a dataset for summarizing text from one language to another for a large collection of languages?

CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs


Question: I am a beginnner to the field of NLP, and I wonder roughly how many papers on average should one paper cite, given the provided paper list.

Your answer should be a single integer number, rounded from the average reference number.


Question: Who are the authors of this paper? What're their institutions?

Your answer should be a Python dictionary, e.g. {"Amy": ["Massachusetts Institute of Technology", "Carnegie Mellon University"], "Bob": ["Shanghai Jiaotong University"]}. YOU MUST USE THE FULL AND EXACT WORDS FROM PDF.

Allen Institute for AI
The Hebrew University of Jerusalem

Allen Institute for AI
Bar-Ilan University


Question: In the paper that proposed the information filtering hypothesis, under non-semantic task setting, how much does the frozen LLM transformers improve VectorNet, considering the miss rate?

Your answer should be a positive float, rounding to 1 decimal place.


Question: In video diffusion models, is there any paper that tried decomposing video instruction into sub instructions of different time?

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

Question: In video diffusion models, is there any paper that tried decomposing video instruction into sub instructions of different time?

Seer: Language Instructed Video Prediction with Latent Diffusion Models


Question: What are the differences between the two GNeRF settings in the experiments of the paper?

Your answer should be a python strings about the obvious differences.

The DTU dataset is used in setting I for training but not used for either training or evaluation in setting II.

In setting I, N = 8. In setting II, N = 10.

Question: What are the differences between the two GNeRF settings in the experiments of the paper?


Question: According to the experimental results, if we remove the document fact attention module and use mean pooling to fuse all document semantic representation vectors, by how much does the F1 score of FINEGRAINFACT decline in summaries generated by pre-trained language models published in or after 2020?

Your answer should be a single python float, rounded to 2 decimal places.


Question: Is there any paper trying to improve MLE for auto-regressive language modeling through the lens of optimal transport?

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

Question: Is there any paper trying to improve MLE for auto-regressive language modeling through the lens of optimal transport?

EMO: EARTH MOVER DISTANCE OPTIMIZATION FOR AUTO-REGRESSIVE LANGUAGE MODELING


Question: Which machine learning paper proposed certified robustness in the malware detection domain?

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

Question: Which machine learning paper proposed certified robustness in the malware detection domain?

DRSM: DE-RANDOMIZED SMOOTHING ON MALWARE CLASSIFIER PROVIDING CERTIFIED ROBUSTNESS


Question: Is there a dataset that allows to perform aspect-based sentiment classification on French news?

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

Question: Is there a dataset that allows to perform aspect-based sentiment classification on French news?

MAD-TSC: A Multilingual Aligned News Dataset for Target-dependent Sentiment Classification


Question: How many GPUs would be required to train a 70M model used in this paper if we had used the batch sizes from the GPT-3 suite?


Question: What challenges in mobile health does RoME handle?

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

participant heterogeneity
nonstationarity
nonlinear relationships

Question: What challenges in mobile health does RoME handle?


Question: How to achieve zero-shot lip reading?

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

Question: How to achieve zero-shot lip reading?

OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment


Question: Has there been any recent work or competitions focused on the development of methods to counteract clickbait through spoiling, such as revealing key information upfront?

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

Question: Has there been any recent work or competitions focused on the development of methods to counteract clickbait through spoiling, such as revealing key information upfront?


Question: In the training of LangBrige, when adapting finetuned LMs, is multilingual encoder trainable?

Your answer should be a Python bool of true or false.


Question: What type of information is scored the highest when the loss equals 0, 0.01 and 0.06? What can be concluded from this result?

Your answer should be a python list of two strings, the first is the type of information, the second is the conclusion from the result.

When there is no information loss, participants evaluate more compressed causal claims significantly more highly than less compressed causal claims. When information loss is moderate, there is no significant difference. When information loss is high, participants prefer less compressed claims.

Question: What can be concluded from this result?


Question: What are the meanings of function $s$ and function $F$ in Equation (3)?

Your answer should be a sentence describing the meanings of function $s$ and function $F$ in Equation (3).

$s(\cdot)$ is the cosine similarity and $F(\cdot)$ is a non-linear mapping on the output representations of PLM.

Question: What are the meanings of function $s$ and function $F$ in Equation (3)?


Question: For the model which suffered the biggest loss of response fidelity from adding image to query, how many hours did its training take? How many AI accelerators were used?

Your answer should be a python list with 2 elements. The elements should be integers, the first one giving the number of hours, and the second one giving the number of accelerators.


Question: Which paper first combines rewriting and expansion methods to reformulate a query for conversational search?

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

Question: Which paper first combines rewriting and expansion methods to reformulate a query for conversational search?

ConvGQR: Generative Query Reformulation for Conversational Search


Question: In the domain of prediciton questions, what's the largest issue for LLAMA 2?

Your answer should be a word or phrase, indicating the largest issue. YOU MUST USE THE EXACT WORDS FROM PDF WITHOUT EXPLANATIONS.


Question: When analyse the statistics of Chinese GEC datasets, which dataset for the usage of Fine-tuning has the largest number of sentences?

Your answer should be a single string of the dataset's name.


Question: What are the 3 primary algorithmic strategies evaluated in the LLMC toolkit for quantizing large language models, and how do they differ in approach?

Question: What are the 3 primary algorithmic strategies evaluated in the LLMC toolkit for quantizing large language models, and how do they differ in approach?

1. Transformation, which modifies the distribution of weights and activations to reduce outliers using techniques like scaling-based transformations (e.g., AWQ, SmoothQuant) and rotation-based methods (e.g., QuaRot); 2. Clipping, which limits extreme weight values through symmetric or asymmetric methods to manage outliers that impact quantization; 3. Reconstruction, which minimizes quantization error by iteratively adjusting unquantized weights, as exemplified by GPTQ, to closely replicate original model outputs.


Question: What paper first proposes that simply reversing the output can significantly enhance the sample efficiency and the performance of the arithmetic capability of a decoder-only Transformer model?

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

Question: What paper first proposes that simply reversing the output can significantly enhance the sample efficiency and the performance of the arithmetic capability of a decoder-only Transformer model?

Teaching Arithmetic to Small Transformers


Question: What are the baseline models used in the experiments of the two most recent papers and this paper?

Your answer should be a python list of elements, each element is the baseline model name string, e.g., ["model1", "model2", ...]. YOU MUST USE THE EXACT NAMES FROM THE PDF WITHOUT CHANGING THE CAPITALIZATION.


Question: In the paper that shares the similar world model loss function with the R2I paper, a competition concerning Minecraft is introduced. Where can I find the data of that competition?

Your answer should be a Python string starting with "https://", the URL of the dataset as given in the paper. You don't need to make sure the URL is still valid, just provide the URL as it is in the paper.


Question: Which backdoor paper first used the CLIP to suppress benign features and enhance poisoning features to design triggers?

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

Question: Which backdoor paper first used the CLIP to suppress benign features and enhance poisoning features to design triggers?

Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios


Question: Which dataset did not get improved performance after applying the proposed RECOST method to Alpaca-gpt4, compared to the Random baseline? Tell me this worst-performing dataset. And what's the remaining performance gap for our best-performing RECOST method compared to the reported human upper bound on the testset for that dataset?

Your answer should be a Python list of two elements, where the first element is the name of the dataset, and the second element is a float number rounded to 2 decimal places, calculated by subtracting the performance of the best-performing RECOST method from the reported human upper bound performance for that dataset.


Question: What data augmentation strategies are used in the recently proposed dataset used in this paper?

Your answer should be a python strings about the detailed data augmentation strategies.

To achieve the balance between the two-way translation in language pairs, two data augmentation strategies were utilized to enrich the corpus if necessary: In cases where the number of parallel corpus falls below 1 million, we flip the entire corpus to create the corpus for the opposite translation direction. In contrast, for corpora with more than 1 million instances, we randomly flip half the amount of corpus to generate the corresponding corpus. After data augmenting, the initial corpus of 142 translation directions is substantially enriched, expanding to a significantly larger corpus of 242 translation directions.

Question: What data augmentation strategies are used in the recently proposed dataset used in this paper?


Question: What are the catogories of label biases in in-context learning for text classification and what are the definitions of these categories?

Your answer should be a Python list of text strings, with each element being one category that this paper defines, e.g., ["category 1: define 1", "category 2: define 2", ...].

vanilla label bias: The model's non-contextualized preference for the label names (e.g., the common token bias caused by different frequencies of label names in the pretraining corpus).

context-label bias: The effects of the context prompt (e.g., LLMs tend to prefer the majority and last label of the in-context examples).

domain-label bias: The effects of the task corpus on the model's predictions.

Question: What are the catogories of label biases in in-context learning for text classification and what are the definitions of these categories?


Question: Which work discusses an analysis of source and target contributions to output generation based on local interpretation when machine translation models experience hallucinations?

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

Question: Which work discusses an analysis of source and target contributions to output generation based on local interpretation when machine translation models experience hallucinations?

Local Interpretation of Transformer Based on Linear Decomposition


Question: Which paper first found that when transformers are trained to in-context learn function classes, they might exhibit generalization followed by memorization, in certain settings?

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

Question: Which paper first found that when transformers are trained to in-context learn function classes, they might exhibit generalization followed by memorization, in certain settings?

In-Context Learning through the Bayesian Prism


Question: According to the paper that proposed Synapse, which other concurrent work performs the best? In the dataset that both papers applied, how many tasks are there?

Your answer should be a Python list of 2 elements, the first is a string, the name of method, and the second is an integer, the number of tasks.


Question: The two anchor PDFs propose the AudioDec and LMCodec models, both improved based on the same codec. What is the name of this codec? Also, do the RVQ parts in both works have the same single codebook size?

Your answer should be a Python list where the first item is the codec name (a string) and the second item is 'yes' or 'no' (a string).


Question: Out of the four baselines used by the paper that proposes MEQE method, which one is also utilized as a baseline by the other three papers? Additionally, what are the two other baselines that the three papers have in common?

Your answer should be a Python list with two elements. The first element should be the full name of the baseline shared by all four papers (the anchor PDF method and the three other baselines). The second element should be a Python list containing the full names of the two additional baselines shared by the three papers. Ensure the baseline names are the full names.


Question: Which paper surveyed the datasets and tasks of asking clarification questions in conversational systems??

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

Question: Which paper surveyed the datasets and tasks of asking clarification questions in conversational systems??

A Survey on Asking Clarification Questions Datasets in Conversational Systems


Question: Which model achieves superior performance with a large number of examples in the task of cancer type classification?

Your answer should be a single model name used in the corresponding figure.


Question: What is a large event-coverage general-domain event argument extraction dataset?

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

Question: What is a large event-coverage general-domain event argument extraction dataset?

GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles


Question: In the experiments, which datasets did the authors use, and how many samples are there in the training set of each dataset?

Your answer should be a Python dictionary, where the keys are the names of datasets and the values are the number of samples in the respective training set. e.g. {\"dataset1\": 10, \"dataset2\": 20, ...} .


Question: In the survey of Large Language Models for NL2Code, what are the multi-lingual benchmarks to evaluate the NL2Code task, and how many instances do they contain per promgramming language?

Your answer should be a Python dictionary of entries, each dictionary key is a string, the benchmark name DIRECTLY FROM THE PDF WITHOUT CHANGING CAPITALIZATION, and each value is an integer of the corresponding instance number, e.g., {"benchmark1": 10, "benchmark2": 100}, ....


Question: Can you tell me the core idea of the source paper of the methodology which inspires the creation of HarmfulQ dataset?

Your answer should be a string about the core idea.

The paper proposes a novel approach to "red teaming" language models (LMs) using other LMs. This means using one LM to generate test cases (input prompts) that can uncover harmful outputs from another LM. The key idea is to leverage the generative capabilities of LMs to automatically generate a large and diverse set of test cases, which can reveal various failure modes and potential harmful behaviors of the target LM. This approach complements manual testing and can help identify and mitigate risks before deploying LMs in real-world applications.

Question: Can you tell me the core idea of the source paper of the methodology which inspires the creation of HarmfulQ dataset?


Question: MMD and xVal, a baseline in the anchor paper, both aim to solve the problem of embedding numbers in language models. Did the tasks focused on by the two papers belong to the same domain? If not, what types of tasks does xVal focus on?

Your answer should be brief text answering whether the tasks focused on by the two papers belong to the same domain, and if not, the domain of the task focused on by xVal.

No, they don't. xVal mainly focuses on prediction tasks involving numbers in the scientific domain.

Question: MMD and xVal, a baseline in the anchor paper, both aim to solve the problem of embedding numbers in language models. Did the tasks focused on by the two papers belong to the same domain? If not, what types of tasks does xVal focus on?


Question: In the main results of ShortGPT's source paper, which paper does the experimental results of several comparison methods come from? Does the method proposed in this paper require post-training?

Your answer should be a python list of two elements. The first element is a python string, the paper's full name. The second element is a python bool.

LaCo: Large Language Model Pruning via Layer Collapse


Question: In the previous work of "Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks" which uses the same game, how many training hours does it take in average for the agent trained with 128 CPU cores to beat the behavior-tree AI?

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


Question: Which one of the prior works on state space models by the same team that published the Mamba paper proposes FlashConv for accelerating state space model training?

Your answer should be a python string, the full paper name of the prior work.

Hungry Hungry Hippos: Towards Language Modeling with State Space Models


Question: What is the optimal number of layers to skip for LLaMA2-13B?

Your answer should be a Python integer number. e.g. 3


Question: What techniques exist for incorporating context in detecting emotions within dialogues by leveraging pre-trained language models?

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

Question: What techniques exist for incorporating context in detecting emotions within dialogues by leveraging pre-trained language models?

Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations


Question: In the experiment section of the paper, what is the detailed procedure of back-translation for sentence reconstruction?

The procedure of back-translation is using a Czech-English NMT system to translate Czech sentences from the training data into English. Then pair the translations with the English references to form English-English paraphrase pairs. We used the pretrained Czech-English model from the NMT system.

Question: In the experiment section of the paper, what is the detailed procedure of back-translation for sentence reconstruction?


Question: What are the selected questionnaires or scales on personality trait domain in the PsychoBench framework?

Your answer should be a python list of the abbreviations of the questionnaires names, e.g., ['NEO-FFI', 'IPIP']


Question: Across different corpuses analysed in the paper, which one has the best word order monotonicity? What's its key feature?

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

With fluency and adequacy verified by professional interpreters, Chunk-wise is manually created following Chunk-Wise Monotonic Translation guideline. A key feature of chunk-wise is its monotonic alignment with the source, maintaining the entire source content, making it well-suited for the goals of SiMT.

Question: What's the key feature of chunk-wise?


Question: According to Figure 3, what are the layers proposed by the paper(compared to existing methods) in the overall framework of AR quality predictions?

Your answer should be a python list, every element of the list is a string presented in the original figure of the paper. If there are multiple layers with the same name, they only need to be mentioned once.

Argumentative Context (AC) Generation
DistilRoBERTa Encoder
ChatGPT
2nd-pass Zero-Shot-CoT Prompt
1st-pass Zero-Shot-CoT Prompt


Question: What is the maximum gain obtained by adding more outputs in all the datasets tested?

Your answer should be a floating point numbers with three decimal places.


Question: What are the three types of instruction-following data, and which one has the largest number of samples?

Your answer should be a Python list of 2 elements. The first element is a Python list of 3 elements, containing the names of the three types of instruction-following data. The second element is a string, indicating the name of the type of instruction-following data that has the largest number of samples. e.g. [["type1", "type2", "type3"], "type"].

conversation
detailed description
complex reasoning


Question: What is the improvement in precision (in percentage points, rounded to one decimal place) from unconstrained to constrained for LLaMA-33B in closed information extraction (4 shots)? And where can I get the testing dataset (the GitHub link)?

Your answer should be a single python list containing two strings, the first element of the list is the improvement in precision (in percentage points, rounded to one decimal place), the second element of the list is the GitHub link of the testing dataset.


Question: How many tasks are in WebArena? Can they be categorized into classes? How many classes can they be categorized into? What are the classes? How many tasks are in theses classes, respectively?

Your answer should be a Python list. The first element is an integer indicating the total task number. The second one is a boolean indicating if the tasks can be categorized. If the second one is true, there should be more elements. The third element should be an integer indicating the class number. The fourth one should be a string list storing the class names. The fifth one should be an integer list storing the task numbers in each class. If any needed information cannot be specified through the paper, give an empty string as the answer for that item.

Information-seeking
Site navigation
Content and configuration operation


Question: Provide an example of a paper which proposes a method to learn a dynamic (conditioned on the input) sequence tokenizer (segmenter) via standard gradient backpropagation.

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

Question: Provide an example of a paper which proposes a method to learn a dynamic (conditioned on the input) sequence tokenizer (segmenter) via standard gradient backpropagation.

Efficient Transformers with Dynamic Token Pooling


Question: What are the key advantages of coupling neural SDEs with neural CDEs for treatment effect estimation over existing baselines?

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

Question: What are the key advantages of coupling neural SDEs with neural CDEs for treatment effect estimation over existing baselines?

BAYESIAN NEURAL CONTROLLED DIFFERENTIAL EQUATIONS FOR TREATMENT EFFECT ESTIMATION


Question: In formula(3), what do r_where and r_what represent? How to estimate whether the brain indicates better performance by WhereCNN or WhatCNN?

Your answer should be a python strings of the representation of rwhere and rwhat and how to identify whether WhereCNN or WhatCNN performs better.

r_where and r_what represent the prediction accuracy, measured as the correlation coefficients between the predicted and actual fMRI responses for the WhereCNN-based and WhatCNN-based encoding models, respectively. p_where > 0.5 indicates better predictive performance by WhereCNN, while p_where < 0.5 indicates better predictive performance by WhatCNN.

Question: In formula(3), what do r_where and r_what represent? How to estimate whether the brain indicates better performance by WhereCNN or WhatCNN?


Question: Is there a paper exploring the curse of multilinguality for similar languages?

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

Question: Is there a paper exploring the curse of multilinguality for similar languages?

Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages


Question: On which datasets were the best-performing Medical MLLMs (excluding the method proposed in this paper) trained and evaluated in the Medical VQA benchmark 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: What open-source dataset combined knowledge retrieval with constraint satisfaction queries?

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

Question: What open-source dataset combined knowledge retrieval with constraint satisfaction queries?

KITAB: EVALUATING LLMS ON CONSTRAINT SATISFACTION FOR INFORMATION RETRIEVAL


Question: Is there a paper which proposes a general data selection method based on information theory?

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

Question: Is there a paper which proposes a general data selection method based on information theory?

GIO: GRADIENT INFORMATION OPTIMIZATION FOR TRAINING DATASET SELECTION


Question: Which foundation model paper first proposed a time series model with proposed financial time series and text data?

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

Question: Which foundation model paper first proposed a time series model with proposed financial time series and text data?

TEMPO: PROMPT-BASED GENERATIVE PRE-TRAINED TRANSFORMER FOR TIME SERIES FORECASTING


Question: Are there any papers that use a world model for planning to ensure that decisions meet constraints?

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

Question: Are there any papers that use a world model for planning to ensure that decisions meet constraints?

SAFEDREAMER: SAFE REINFORCEMENT LEARNING WITH WORLD MODELS


Question: In multimodal (multilingual) abstractive summarization field, is there any paper that propose target-oriented vision modeling method to improve the quality of summaries?

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

Question: In multimodal (multilingual) abstractive summarization field, is there any paper that propose target-oriented vision modeling method to improve the quality of summaries?

Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization


Question: When answering the question "Are ExNLP tasks associated with high-risk situations?", which paper does this paper("On Evaluating Explanation Utility for Human-AI Decision Making in NLP") learn from? In the experiment of the source paper, how many types of knowledge-context are there?

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

Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs


Question: Which base model does this paper("Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning") train as the retrieval model for the SA task? In its source paper, on how many STS tasks is it evaluated?

Your answer should be a python list of two strings, the first element is the model name(one word), and the second element is an integer number.


Question: Which paper measured how well the source-translation contribution by the translation model can be used to detect its own hallucinations?

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

Question: Which paper measured how well the source-translation contribution by the translation model can be used to detect its own hallucinations?

Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better


Question: In which website can I find the information of the benchmark used to compare multilingual models? In which conference was the mT5 reference paper included in the benchmark published?

Your answer should be a python list of two strings, the name of the website and the name of the conference.


Question: When we utilize decoder-only language models in understanding word meaning, does prompting styles affect performance? If so, which technique outperforms the others? If not, what is the worst one?

Your answer should be a Python list of two elements, the first element is "yes" or "no", and the second element is the prompting style name string, don"t reply abbreviations, e.g., ["yes", "prompting_style_name"].


Question: Which model gets the highest DR value in the Random Retrieval performance?

The BART-base model gets the highest DQ value.

Question: Which model gets the highest DR value in the Random Retrieval performance?


Question: How to find those question samples that the model considers to be ambiguous.

First, let the model generate an initial answer based on the question, then prompt the model to eliminate ambiguity and generate a second answer. If the model's average entropy of the two answers exceeds a threshold, it indicates that this question is ambiguous.

Question: How to find those samples that the model considers to be ambiguous.


Question: What's the biggest reason of incorrect action for each model?

Your answer should be a Python dictionary. e.g. {"model1": "answer1", "model2": "answer2", ...}. YOU MUST USE THE EXACT AND FULL TEXT FROM PDF WITHOUT CHANGING CAPITALIZATION.


Question: Which paper first study POMDP with enhanced feedback on observations?

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

Question: Which paper first study POMDP with enhanced feedback on observations?

Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight


Question: In the larger dataset in hours that the EgoDistill paper applies, what's the second largest country of residence for camera wearers?

Your answer should be a string, the country as given in the paper.


Question: What are the differences in the composition of the Primary System for the unconstrained setting between the 2023 and 2024 QUESPA Submissions?

The Primary System in 2024 for the unconstrained setting consists of a pre-trained model called SpeechT5, and The Primary System in 2023 for the unconstrained setting consists of two systems, the ASR and the MT system.

Question: What are the differences in the composition of the Primary System for the unconstrained setting between the 2023 and 2024 QUESPA Submissions?


Question: When training with non-English image-text pairs, what is the loss function of the TriKD?

Your answer should be a sentence describing the loss function of the TriKD when training with non-English image-text pairs, including the terms involved in the loss function and their meanings, as given in the paper.

When training with non-English image-text pairs, only ITC loss is applied, as the CLIP text encoder does not support non-English languages. Therefore, the loss function for the TriKD is $\mathcal{L}_{\text{TriKD}} = \mathcal{L}_{\text{ITC}}$. And the Image-Text Contrastive(ITC) loss is formulated as the average of image-to-text($\mathcal{L}_{\text{i2x}}$) loss and text-to-image($\mathcal{L}_{\text{x2i}})$) loss: $\mathcal{L}_{\text{ITC}} = 1/2(\mathcal{L}_{\text{i2x}} + \mathcal{L}_{\text{x2i}}) = 1/2[\ell(h^I, h^X) + \ell(h^X, h^I)]$. Here, $h^I$ represents the $\ell_2$-normalized output from the CLIP image encoder and CLIP-projector, and $h^X$ represents the $\ell_2$-normalized output from the Multilingual Text Encoder (MTE) and X-projector for a given image-text pair.

Question: When training with non-English image-text pairs, what is the loss function of the TriKD?


Question: What are the seven categories of tasks, which form the dataset used to conduct SFT on a Llama-2-7B model in section 2.1?

Your answer should be a Python list of seven elements, containing the names of the seven categories of tasks. e.g. ["task1", "task2", ... "task7"]. YOU MUST USE THE EXACT AND FULL NAMES OF THE TASKS AS MENTIONED IN THE PAPER.

closed-book question answering
coreference resolution
natural language inference
abstract summarization
multi-lingual translation
reading comprehension
text classification


Question: What are the main models mentioned in the anchor_pdf and what is the relationship between them?

The main models mentioned in the anchor_pdf are T0, T5 and Tk-INSTRUCT. T0 and Tk-INSTRUCT are based on T5 and have been improved.

Question: What are the main models mentioned in the anchor_pdf and what is the relationship between them?


Question: Which sub-splits are included in the validation and test sets of the OC20 dataset used in the paper?

Your answer should be a python list of the full names of the sub-splits. YOU MUST USE THE EXACT FULL NAMES FROM THE PAPER.

in-distribution adsorbates and catalysts

out-of-distribution adsorbates and catalysts


Question: What are the sources of the pre-training data for the latest LLM used in the experiment section of the paper "AN UNFORGEABLE PUBLICLY VERIFIABLE WATERMARK FOR LARGE LANGUAGE MODELS"?

Your answer should be a python list of strings, e.g., ["source1", "source2"].

CommonCrawl C4 Github Wikipedia Books ArXiv StackExchange


Question: Is there a paper that links exposure bias to distillation?

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

Question: Is there a paper that links exposure bias to distillation?

A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training


Question: Which two datasets it combines, the dataset that Alchemist used to evaluate image modality?

Your answer should be a Python list of two strings, the abbreviations of the datasets as given in the paper.


Question: Which papers develop methods to make in-context learning more computationally efficient?

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

Question: Which papers develop methods to make in-context learning more computationally efficient?

FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning


Question: In the largest dataset concerning procedural graph extraction before PAGED, how many labeled sentences are there in total?


Question: Which paper proposes to integrate black-box LLMs with a pool of smaller but specialized language models?

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

Question: Which paper proposes to integrate black-box LLMs with a pool of smaller but specialized language models?

KNOWLEDGE CARD: FILLING LLMS' KNOWLEDGE GAPS WITH PLUG-IN SPECIALIZED LANGUAGE MODELS


Question: In the GenRec paper, which dataset used in the experiment is not evaluated in Table 1? Additionally, what's the range of the clip length for that dataset?

Your answer should be a Python list of 2 elements, the first is a string, the name of the dataset, and the second is a Python list of 2 floats, the range of the clip length, rounded to 2 decimal places, in seconds, e.g. ["dataset", [1.01, 2.02]]


Question: In the respective main experiment of SLED and DoLa, do they use the same evaluation datasets?Do they use the same model family?

Your answer should be a list of two integers, where the first integer is 1 if the evaluation datasets are the same and 0 otherwise, and the second integer is 1 if the model family is the same and 0 otherwise.


Question: What is the first paper to address the problem of predicting knowledge graphs whose nodes, links and attributes change with time?

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

Question: What is the first paper to address the problem of predicting knowledge graphs whose nodes, links and attributes change with time?

Holistic Prediction on a Time-Evolving Attributed Graph


Question: According to Table 2 in the paper "ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought", how many times does the LLM's API need to be called to generate a SQL query in the DIN-SQL approach? What modules in the DIN-SQL approach lead to those API calls?

Your answer should be a Python list like [integer, string1, string2, ...]. The first element should be an integer, representing the number of times the LLM's API needs to be called. Each subsequent element should be a string, representing a module name in the DIN-SQL approach. Note that the module names do not need to include the word "module".

schema linking
classification & decomposition
sql generation
self-correction


Question: According to the MathCAMPS paper, among the models evaluated, which one performs the second best on MathCAMPS grade 8? In the paper that proposed the model, how is the output y computed, given an input token x?

Your answer should be a Python list of 2 elements, the first is the name of the model along with its parameter size as given in the paper, and the second is the formula in LaTeX format.

y = \sum_{i=0}^{n-1} \text{Softmax}(\text{Top2}(x \cdot W_g))_i \cdot \text{SwiGLU}_i(x)

Question: How is the output y computed, given an input token x?


Question: In the paper that proposed the model that is applied in TabMT for subtle pattern detection, how many instances are there in the dataset where the proposed model performs the best?


Question: I want to contact the first author of this paper. What's the email address?

Your answer should be a verbose text string representing the email address if there is only one first author. Otherwise, return a Python list of e-mail strings for each first and co-first author, e.g., ["xxx@xxx.com", "yyy@yyy.com", ...]. DO NOT INCLUDE ANY OTHER CONTEXT IN YOUR ANSWER.


Question: Why can we omit $p(\{y_l, l \in L\})$ in Equation (1)?

Your answer should be a sentence explaining why we can omit this term.

In Equation (1), $p(\{y_l, l \in L\})$ can be omitted because it acts as a normalization factor that is constant with respect to the ancestral form $x$. The goal of the equation is to compute a value proportional to the posterior probability $p(x | \{y_l, l \in L\})$. Since $p(\{y_l, l \in L\})$ does not depend on $x$, it remains the same for all possible ancestral forms and thus does not affect the relative probabilities of different $x$ values.

Question: Why can we omit $p(\{y_l, l \in L\})$ in Equation (1)?


Question: The anchor PDF used two benchmark datasets for evaluation. Overall, on which dataset did the methods perform better?

Your answer should be a python strings about the name of the dataset. YOU MUST USE THE EXACT NAME FROM THE PAPER.


Question: In the entity detection experiments, what is the text type of the dataset used in the training stage with highest F-Score_test?

Your answer should be a short word or phrase.


Question: Which paper explored training a GPT-2 for automatic diagnosis, emphasizing efficient data augmentation for symptom prediction and disease identification?

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

Question: Which paper explored training a GPT-2 for automatic diagnosis, emphasizing efficient data augmentation for symptom prediction and disease identification?

CoAD: Automatic Diagnosis through Symptom and Disease Collaborative Generation


Question: Is there a paper comparing knowledge distillation and human annotation in terms of cost efficiency?

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

Question: Is there a paper comparing knowledge distillation and human annotation in terms of cost efficiency?

Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models


Question: Is there any paper that seamlessly integrates the multigrid structure in operator learning for solving partial differential equations (PDEs)?

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

Question: Is there any paper that seamlessly integrates the multigrid structure in operator learning for solving partial differential equations (PDEs)?

MgNO: Efficient Parameterization of Linear Operators via Multigrid


Question: Which paper is the first to comprehensively review the progress of deep learning in mathematical reasoning?

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

Question: Which paper is the first to comprehensively review the progress of deep learning in mathematical reasoning?

A Survey of Deep Learning for Mathematical Reasoning


Question: Among the specific models tested, whose performance is closest to RePe on MixATIS++?

Your answer should be the name of model DIRECTLY FROM THE PDF WITHOUT ANY EXPLANATION.


Question: In the anchor PDF, the authors introduce two key changes relative to the original work. Which part are they located in Figure 1? Additionally, are there any changes in the blue boxed area in Figure 1? Please briefly describe these changes.

Your answer should be a Python list with two strings: the first is a location option (from top-left, top-right, bottom-left or bottom-right), and the second is your answer to the remaining question.

There are changes. The architecture of the content encoder changed from Transformer to Conformer; the energy predictor was removed from the variance adapter; the decoder changed from Transformer-based decoder to diffusion decoder.

Question: Are there any changes in the blue boxed area in Figure 1? Please briefly describe these changes.


Question: In the paper that proposes NoMAD-Attention, what do the authors choose as vector database? Additionally, in the paper that proposes that vector database, how to compute the number of distance computations and when it reaches a minimum?

Your answer should be a Python list of 2 strings, each string is a formula in LaTeX format, representing the equation of the number of distance computations and the condition when it reaches a minimum.

N_{\text{distances}} = K_\text{IVF} + P_\text{IVF} \times N/K_\text{IVF}

Question: How to compute the number of distance computations and when it reaches a minimum?


Question: How much improvement does "Dr.Strategy" achieve in "Maze-7*7" in average?

Your answer should be a Python float number rounded to 2 decimal place. e.g. 11.45


Question: GeoBFN (Geometric Bayesian Flow Network) handles data from which three main modes in 3D molecule generation? What are the modeling characteristics of the first of these modalities (atomic coordinates) in GeoBFN?

Your answer should be two sentences, each answers a question.

The three main modes are atomic coordinates, atomic charge and atomic type.

Atomic coordinates are continuous variables and GeoBFN optimizes them in parameter space via a Bayesian flow network and models their distribution via a Gaussian distribution while maintaining SE(3) invariance.

Question: GeoBFN (Geometric Bayesian Flow Network) handles data from which three main modes in 3D molecule generation? What are the modeling characteristics of the first of these modalities (atomic coordinates) in GeoBFN?


Question: In the paper that proposes a margin-based satisficing imitation learning method that autonomously surpasses human demonstrators' aspiration levels rather than rigidly mimicking suboptimal behaviors, which method in Table 1 achieves the highest \gamma-satisficing value for the "cartpole" environment, and what is the value?

Your answer should be a Python list of two elements, the first is the name of the method and the second is a float number (rounded to 2 decimal places) of the value, the formula in LaTeX format.


Question: A paper demonstrate the high accurancy of posterior hallucination rate in estimation of the actual probability of hallucination. In the visualization of individual PHR and THR predictions at different context lengths, under which length of context, they show the less linearity?


Question: What's the base pre-trained model used in this paper? For this pre-trained model, what's the pre-train dataset?

Your answer should be a single python list like this: ["model_name", ["dataset_name1","dataset_name2"]]. Note that for theses names, the abbreviation is required.


Question: In ICLR 2024 Poster papers, a paper tries to ensemble the reward models to mitigate the over-optimization problem. What is the formula of the reward model?

Your answer should be the formula in LaTeX format.

Question: In ICLR 2024 Poster papers, a paper tries to ensemble the reward models to mitigate the over-optimization problem. What is the formula of the reward model?

\underbrace{\frac{1}{k}\sum_{i} R_i(q, a)}_{\text{mean}} - \lambda \underbrace{\frac{1}{k}\sum_{i} \left( R_i(q, a) - \frac{1}{k}\sum_{i} R_i(q, a) \right)^2}_{\text{variance}}


Question: In the paper that proposes a task-oriented imputation framework that evaluates and optimizes time series filling strategies based on their direct performance gains in downstream tasks without model retraining, what is the key assumption of formula (9) to compress the size of \frac{\partial f(X_i, \theta)}{\partial \theta}?

Your answer should be a Python strings of the key assumption.

The author assumes that the model output f(X_i, \theta) resides in a low-dimensional space spanned by a limited number of smooth basis functions.

Question: In the paper that proposes a task-oriented imputation framework that evaluates and optimizes time series filling strategies based on their direct performance gains in downstream tasks without model retraining, what is the key assumption of formula (9) to compress the size of \frac{\partial f(X_i, \theta)}{\partial \theta}?


Question: In the paper that proposes Variational BoN, where can I find the binary classifier that the author uses with two classes {POS, NEG} as the reward model?

Your answer should be Your answer should be a Python string, the website URL starting with "https://", as given in the paper.


Question: In ICLR 2024 Oral papers, which paper proposes a novel unsupervised RL objective, which authors call Metric-Aware Abstraction (METRA)?

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

Question: In ICLR 2024 Oral papers, which paper proposes a novel unsupervised RL objective, which authors call Metric-Aware Abstraction (METRA)?

METRA: Scalable Unsupervised RL with Metric-Aware Abstraction


Question: In the paper that integrates Foundation Models, Federated Learning, and Blockchain into a unified framework for smart cities, what is the institution of the first author of this paper?

Your answer should be a python strings of the name of the institution.

Department of Artificial Intelligence HI Iberia (HIB)

Question: In the paper that integrates Foundation Models, Federated Learning, and Blockchain into a unified framework for smart cities, what is the institution of the first author of this paper?


Question: A recent paper introduces BrainMD, a large-scale multimodal dataset comprising 2,453 3D MRI brain scans paired with radiology reports and longitudinal health records, designed to evaluate vision-language models (VLMs) on medical imaging tasks. Based on the experimental results presented in this work regarding BrainMD, could you please tell me which model among [Flamingo, Med-Flamingo, Med-PaLM-2] demonstrates the highest performance in identifying cancer type?

Your answer must be one of the following: ['Flamingo', 'Med-Flamingo', 'Med-PaLM-2']


Question: In the paper that proposes I-Frame Domain Adaptation in Neural Video Compression,the author modifies the \gamma_d and \gamma_u to the trade-off between parameter efficiency and performance improvement. Which pair of \gamma_d and \gamma_u has the least training parameters added compared to the base model?

Your answer should be a Python list of two integers indicating \gamma_d and \gamma_u respectively.


Question: In ICLR 2024 Spotlight papers, a paper attempts to solve how to train a general agent in Reinforcement Learning (RL) that can thoroughly explore the environment and learn new and diverse skills. What is the affiliation of the second author?


Question: There is a recent paper introducing a large-scale, long-term, semantically annotated outdoor dataset collected across the USC campus using a mobile robot equipped with multi-camera and LiDAR sensors. It features 10 million images and 1.4 million point clouds annotated with 267 semantic classes using GPT-4 and Grounded-SAM, enabling fine-grained 3D scene understanding. Please inform me which semantic label has a higher point frequency in this dataset.

Your answer should be a name of a semantic label.


Question: In NeurIPS 2024 Poster papers, a paper proposes a new problem in offline MBRL called "The Edge-of-Reach Problem". In Figure 5, which two kinds of statistical visualization method are used?

Question: In NeurIPS 2024 Poster papers, a paper proposes a new problem in offline MBRL called "The Edge-of-Reach Problem". In Figure 5, which two kinds of statistical visualization method are used?

The two kinds of statistical visualization methods are the box plot and the histogram.


Question: A paper focus on generating 3D molecular conformers conditioned on molecular graphs in a multiscale manner to study the way that diffusion models process 3D geometries in a coarse-to-fine manner. In their analyze of the spectral domain of Equivariant Blurring Diffusion (EBD), smaller or higher eigenvalues correspond to finer details?

Your answer should be chosen between "smaller" and "higher".


Question: In the paper that proposed the LMSYS-Chat-1M dataset, according to the sampled conversations, which category contains the most clusters?

Your answer should be a string, the category tag as shown in the corresponding figure.


Question: There is a paper proposing Hybrid-LITE, a memory-efficient hybrid retriever that combines BM25 with a novel dense retriever called LITE. LITE is trained using joint contrastive learning and knowledge distillation from DrBoost, a boosting-based dense retriever. May I ask which lab collaborated with Arizona State University on this work?


Question: In ICLR 2024 Spotlight papers, a paper unifies reinforcement learning and imitation learning methods under a dual framework. How many pages are there in this paper?


Question: What's the forward process of HydraLoRA?

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

Question: What's the forward process of HydraLoRA?


Question: In which mathematical subjects, the ablation model trained on 512K instances has the highest accuracy?

Your answer should be a string, which indicates a subject.


Question: In ICLR 2024 Poster papers, a paper proposes a novel framework named PARL (Policy Alignment in Reinforcement Learning), aiming to address the policy alignment problem in Reinforcement Learning (RL). Tell me the affiliated university of the first author.


Question: Which paper published in ICLR 2024 formulates the reference-free MT evaluation into a pairwise ranking problem?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper published in ICLR 2024 formulates the reference-free MT evaluation into a pairwise ranking problem?

MT-Ranker: Reference-free machine translation evaluation by inter-system ranking


Question: Which paper published in ICLR 2024 proposes EControl, a novel mechanism that can regulate error compensation by controlling the strength of the feedback signal?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Which paper published in ICLR 2024 proposes EControl, a novel mechanism that can regulate error compensation by controlling the strength of the feedback signal?

EControl: Fast Distributed Optimization with Compression and Error Control


Question: What is the role of mSDF and how does G-SHELL's extraction algorithm integrate SDF and mSDF?

Your answer should be a sentence answering the two questions.

We define a manifold signed distance field (mSDF) on the watertight template, in which the sign indicates whether a point lies in the open surface or not (\nu(x) > 0, \quad \forall x \in \operatorname{Interior}(M_0); \quad \nu(x) = 0, \quad \forall x \in \partial M_0; \quad \nu(x) < 0, \quad \text{otherwise}), and the absolute scale indicates the geodesic distance to the boundary. An open surface can now be extracted via isoline extraction with mSDF.

With SDF and mSDF values stored in the same 3D grid, we obtain for G-SHELL an efficient Marching-Cubes-like algorithm which reuses the interpolation coefficient for the mSDF sign computation. Specifically, with an edge (p_i, p_j), the corresponding SDF values s_i < 0 < s_j and mSDF values \nu_i, \nu_j, we can compute the mSDF value on the extracted mesh vertex as \nu'

Question: What is the role of mSDF and How does G-SHELL's extraction algorithm integrate SDF and mSDF?


Question: Among the papers at ACL 2023 focusing on cross-lingual transfer learning, what is the average improvement in cross-lingual classification accuracy achieved by the X-InSTA method compared to random prompt selection?

Your answer should be a Python float value between 0 and 100, representing the percentage point improvement, rounded to 1 decimal place.


Question: In ICLR 2024 Poster papers, a paper proposes a reward smoothing method called "DreamSmooth". How many different tasks are illustrated in Figure 5?


Question: In the paper of titled "CAUSAL CONFUSION AND REWARD MISIDENTIFICA TION IN PREFERENCE-BASED REWARD LEARNING", how is the the difference between reward functions measured? Please give me the relevant github link.

Your answer should be a single python string like "https://github.com/a/b", the link should be the full link of the github repository.


Question: In the paper that proposes MULAN, which model in autoregressive type has the highest likelihood in bits per dimension on the test set of ImageNet?

Your answer should be a Python strings indicating the name of the model.


Question: In ICLR 2024 Poster papers, a paper proposes a new Adversarial Imitation Learning (AIL) algorithm, aiming to address the sample efficiency and scalability issues of existing AIL methods when dealing with off-policy data. Tell me the title of this paper.

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

Question: In ICLR 2024 Poster papers, a paper proposes a new Adversarial Imitation Learning (AIL) algorithm, aiming to address the sample efficiency and scalability issues of existing AIL methods when dealing with off-policy data. Tell me the title of this paper.

Adversarial Imitation Learning via Boosting


Question: A recent paper introduces a publicly available multi-granularity dataset for job skill demand forecasting, compiled from millions of online job advertisements. It uniquely supports forecasting at the occupation, company, and regional levels, and includes comprehensive benchmarks across statistical, deep learning, and graph-based models. Please let me know which institution the first author of this work is affiliated with.

Your answer should be a name of an institution.

University of Science and Technology of China


Question: A paper introduce a entirely data-driven relighting method, where intrinsics and lighting are each represented as latent variables. In the experiments of sensitivity to light changes, which model is sensitive to lighting changes?

Your answer should be a string, aneme of model.


Question: In ICLR 2024 Poster papers, a paper proposes a framework where an agent first learns a sufficient set of skill primitives to achieve all high-level goals in its environment. How many baselines are compared in Figure 2?


Question: In ICLR 2024 Poster papers, a paper mainly studies how to more effectively utilize data augmentation techniques to improve sample efficiency and generalization ability in image-based deep reinforcement learning (DRL). Tell me the definition of Q-invariance of this paper.

Your answer should be the formula in LaTeX format.

Question: In ICLR 2024 Poster papers, a paper mainly studies how to more effectively utilize data augmentation techniques to improve sample efficiency and generalization ability in image-based deep reinforcement learning (DRL). Tell me the definition of Q-invariance of this paper.

Q(s, a) = Q(f_{\tau}(s), a) \ for \ all \ s \in \mathcal{S}, a \in \mathcal{A}


Question: What percentage of the MEPS dataset is in the test set?

Your answer should be a float indicating the percentage, rounded to 1 decimal place.


Question: Among the papers at ACL 2023 researching grammatical error correction, what $\text{F}_{0.5}$ score does GEC-DePenD (SUNDAE) achieve on the CoNLL-2014 test set?

Your answer should be a Python float value representing the $\text{F}_{0.5}$ score, rounded to 1 decimal place.


Question: What are the three knowledge selectors shown in Figure 1, and where are they positioned in the workflow? How does the Factuality Selector combine two types of scores to filter knowledge documents?

Your answer should be a sentence answer the two questions.

The three selectors are Relevance Selector, Pruning Selector, and Factuality Selector, applied sequentially after knowledge card generation.

It averages summarization factuality (consistency with original documents) and retrieval-augmented fact-checking (support from external sources) scores, then samples top-ranked documents.

Question: What are the three knowledge selectors shown in Figure 1, and where are they positioned in the workflow? How does the Factuality Selector combine two types of scores to filter knowledge documents?


Question: What is the key energy decomposition formula used in LED-GFN?

E(x) \approx \Phi_\theta(\tau) = \sum_{t=0}^{T-1} \phi_\theta(s_t \rightarrow s_{t+1}),

Question: What is the key energy decomposition formula used in LED-GFN?


Question: In the experiment of the paper that introduces MTOB dataset, how many models managed to achieve 30 chrF score, for kgv to eng task under W+S+G^S setting?

Your answer should be a integer, the number of models.


Question: What's the base model of MERU? Which institute is this base model from?

Your answer should be a single python list like this: ["model_name", "institute_name"]. Note that for both of these names, the abbreviation is required. For example, if the model name is "OpenAI GPT-3" and the institute name is "OpenAI", then your answer should be ["GPT-3", "OpenAI"].


Question: There is a paper that introduces TRAC, a benchmark suite comprising four fundamental reasoning tasks (Projection, Executability, Plan Verification, Goal Recognition) aimed at the textual understanding of preconditions and effects in dynamic environments. In its experiments, the performance of RoBERTa-base on the four sub-tasks shows a stable increase with the growing number of training samples. The question is: which sub-task of TRAC does the RoBERTa-base model achieve the highest performance with 10,000 training samples?

Your answer should be the exact name of the sub-task, must be one of ['Projection', 'Executability', 'Plan Verification', 'Goal Recognition']


Question: In ICLR 2024 Spotlight papers, a paper tries to solve the performance issues of the DICE (DIstribution Correction Estimation) method in offline reinforcement learning (RL) and imitation learning (IL). How many different affiliation of all authors?


Question: Is there any oral work that is related to apply LORA to long context fine-tuning?

Your answer should be a text string representing the title of the work.

Question: Is there any oral work that is related to apply LORA to long context fine-tuning?

LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models


Question: The lack of task specific knowledge or rely on ground truth as few-shot samples is one of the causes of poor performance of traditional learning-based approaches. Therefore, a paper  propose a novel approach called Progressive Retrieval Augmented Generation (P-RAG). This models' success rate is satured more quickly with less rounds of iteration in the ALFRED Valid Unseen dataset or the ALFRED Train 100 dataset?

Your answer should be chosen between "ALFRED Valid Unseen dataset" and "ALFRED Train 100 dataset"


Question: In ICLR 2024 Poster papers, which paper proposes to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models, leveraging the latter to directly regularize the policy gradient with the behavior distribution's score function during optimization?

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

Question: In ICLR 2024 Poster papers, which paper proposes to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models, leveraging the latter to directly regularize the policy gradient with the behavior distribution's score function during optimization.?

Score Regularized Policy Optimization through Diffusion Behavior


Question: In NeurIPS 2024 Poster papers, a paper proposes a methods called "BECUASE". What is the affiliation of the first author?


Question: A recent paper introduces a Transformer variant that enhances computational efficiency and performance by dynamically pooling variable-length token segments during intermediate layers. The paper conducts experiments on the memory consumption of a training step for different shortening factors using the English text8 dataset. With a shortening factor of 3, how much GPU memory in GB is required?

Your answer should be a python float, rounded to 1 decimal place.


Question: A recent paper introduces ProGraph, a benchmark designed to evaluate large language models (LLMs) on graph analysis tasks using external APIs, aligning their behavior with that of human experts. Among the four question types in ProGraph (True/False, Drawing, Calculation, and Hybrid), which category exhibits the highest average difficulty according to the experimental results under the RAG7 settings?

Your answer must be one of the following: ['True/False', 'Drawing', 'Calculation', 'Hybrid']


Question: A paper studies a realistic Continual Learning (CL) setting and applies it to large-scale semi-supervised Continual Learning scenarios with sparse label rate. They show models' performance on 1% labeled ImageNet10k with varying computational steps and conclude DietCL's alleviation of overfitting, how this advantage is reflected in the figure?

Your answer should be a phrase which explains the question with phenomenon showed in figure.

With a large computational steps, the model do not always maintain its high accurancy of fitting.

Question: In the figure which shows models' performance on 1% labeled ImageNet10k with varying computational steps under semi-supervised method, how DietCL's advantage of alleviation of overfitting is reflected?


Question: In the paper that proves a phase transition in attention mechanisms from positional to semantic in language modelsand shows it emerges only beyond a critical data threshold, tell me the number of the authors.

Your answer should be an integer indicating the number of the authors.


Question: what are the two main integration approaches proposed in KNOWLEDGE CARD? How does one differ from the other in terms of knowledge card activation?

Your answer should be a sentence answer the two questions.

The two approaches are Bottom-Up and Top-Down integration.

The Bottom-Up approach activates all knowledge cards at once for multi-domain synthesis, while Top-Down selectively activates relevant cards based on the LLM's iterative decision-making process.

Question: what are the two main integration approaches proposed in KNOWLEDGE CARD? How does one differ from the other in terms of knowledge card activation?


Question: What is the same core conclusion "Which Layer is Learning Faster? A Systematic Exploration of Layer-wise Convergence Rate for Deep Neural Networks" and "On the Spectral Bias of Neural Networks" draw?

The conclusion is Deep Neural Networks (DNNs) exhibit significant variation in learning speeds across different layers, and this disparity is closely linked to spectral bias and DNNs' training follows a "shallow-to-deep" progression that shallow layers stabilize quickly due to their role in low-frequency feature extraction, while deeper layers (near the output) converge more slowly as they handle high-frequency or complex patterns.

Question: What is the same core conclusion "Which Layer is Learning Faster? A Systematic Exploration of Layer-wise Convergence Rate for Deep Neural Networks" and "On the Spectral Bias of Neural Networks" draw?


Question: In the paper at NeurIPS 2024 that introduces LLM landscape for safety, what is the minimum number of adversarial examples required to compromise the safety alignment of GPT-3.5 Turbo through fine-tuning?

Your answer should be a Python integer representing the minimum number of adversarial examples required.


Question: A recent paper introduces PertEval, a toolkit designed to assess the real knowledge capacity of large language models (LLMs) through knowledge-invariant perturbations. Could you please retrieve the article and provide the corresponding GitHub repository link for this work?

Your answer should be a link only without any additional prefixes or suffixes.


Question: In the paper that proposes DFA-GNN, what is the use of the first and the second term in formula (7)?

Your answer should be a Python strings indicating the use of the two terms.

The first term enhances the smoothness of the error estimation throughout the graph, while the second term ensures that the final solution stays consistent with the initial error estimate E.

Question: In the paper that proposes DFA-GNN, what is the use of the first and the second term in formula (7)?


Question: A paper introduce Self-Calibrating Conformal Prediction to recognize the complementary roles of point and interval predictions. In their experiment of comparing SC-CP with baselines, which baseline under-adapts due to insufficient bins?

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


Question: A recent paper proposes a novel reinforcement learning-based approach, DPPO-PR2, for UAV local path planning, demonstrating superior convergence speed and planning performance across six simulated environments. The proposed algorithm is an optimized variant of a classical reinforcement learning algorithm. Which algorithm does DPPO-PR2 build upon?


Question: Among the Findings papers at ACL 2023 researching text summarization, what is the average improvement in ROUGE scores achieved by the proposed framework in the paper "Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation"?

Your answer should be a Python integer representing the maximum improvement in ROUGE scores in percentage points.


Question: Many complex high-dimensional physical systems are recently modeled by graph neural network (GNN) models. In the simulation of the stress on the falling ball and plate after a collision, MGN or GT has a better performance?

Your answer should be chosen between "MGN" and "GT".


Question: What is the overall accuracy achieved by GPT-4V on the MathVista benchmark, and how does it compare to the second-best performing model?

Your answer should be a Python tuple of two float values: [gpt4v_accuracy, difference_from_second_best], both between 0 and 100, rounded to 1 decimal place and represented as percentages.


Question: In the SDXL paper, the c_crop parameter proposed in figure 5 aim to solve which problem?

Your answer should be a paragraph, indicating the problem.

As collating a batch in DL frameworks such as PyTorch requires tensors of the same size, a typical processing pipeline is to (i) resize an image such that the shortest size matches the desired target size, followed by (ii) randomly cropping the image along the longer axis. While random cropping is a natural form of data augmentation, it can leak into the generated samples, causing the malicious effects shown above.

Question: In the SDXL paper, the c_crop parameter proposed in figure 5 aim to solve which problem?


Question: A paper propose a learning paradigm that directly establishes causation between events in the course of time. According to its diabetes simulator, SI1 and GS1 could probablely have the common effect or the opposite effect?

Your answer should be chosen between "common" and "opposite".


Question: There is a paper that introduces a lightweight, model-agnostic alignment framework called Aligner, which corrects residuals between preferred and dispreferred responses using a small plug-and-play model. In this paper, the authors investigate the effects of different identity mapping proportions on the model's helpfulness and harmlessness. Which model consistently achieved the highest scores in the experimental results?

Your answer should be a name of the model.


Question: In ICLR 2024 Oral papers, a paper presents PTGM, a novel method that pre-trains goal-based models to augment RL by providing temporal abstractions and behavior regularization. Tell me the overall objective for training the high-level policy is to maximize the expected return.

Your answer should be formula in the Latex format.

Question: In ICLR 2024 Oral papers, a paper presents PTGM, a novel method that pre-trains goal-based models to augment RL by providing temporal abstractions and behavior regularization. Tell me the overall objective for training the high-level policy is to maximize the expected return.

J(\theta) = \mathbb{E}_{\pi_{\theta}} \left[ \sum_{t = 0}^{\infty} \gamma^t \left( \sum_{i = kt}^{(k + 1)t} R(s_i, a_i) - \alpha D_{\text{KL}} \left( \pi_{\psi}^p(a^h | s_{kt}) \| \pi_{\theta}(a^h | s_{kt})\right) \right) \right]


Question: In YOCO, what's the detailed formulas for self-decoder?

Your answer should be a Python list of two strings, the formulas in LaTeX format.

Question: In YOCO, what's the detailed formulas for self-decoder?


Question: A recent paper introduces a large-scale synthetic dataset of muscle activations derived from biomechanical simulations using OpenSim, encompassing 227 subjects and 402 muscle strands. By enriching motion capture data with simulated muscle activations, the authors bridge the gap between observable motion and internal biomechanics. In this dataset, which action within Dynamic Actions has the highest prevalence?

Your answer should be a name of an action.


Question: Which paper designs a world model for continuous control with "SimNorm"?

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

Question: Which paper designs a world model for continuous control with "SimNorm"?

TD-MPC2: Scalable, Robust World Models For Continuous Control


Question: A paper report the phenomenon that  a small number attention heads transport a compact representation of the demonstrated task, which they call a function vector (FV). From results across layers for the zero-shot case, after adding the function vecteurs, in the lastest layers, do the models get a significantly higher accurancy comparing with models without FV?


Question: Can you recommend me a paper published in ICLR 2024 that proposes a novel model, CONSISGAD, which is tailored for Graph Anomaly Detection in scenarios characterized by limited supervision?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Can you recommend me a paper published in ICLR 2024 that proposes a novel model, CONSISGAD, which is tailored for Graph Anomaly Detection in scenarios characterized by limited supervision?

Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision


Question: In ICLR 2024 Poster papers, a paper attempts to solve the credit assignment problem in Preference-based Reinforcement Learning (PbRL). How many different methods are compard in Figure 2


Question: Does the paper include any diagrams illustrating the relationship between projected-gradient norms and exploitability? If so, what key insight does the diagram convey about bounding exploitability using Lemma 2?

Your answer should be a sentence answering the two questions.

Question: Does the paper include any diagrams illustrating the relationship between projected-gradient norms and exploitability? If so, what key insight does the diagram convey about bounding exploitability using Lemma 2?


Question: In ICLR 2024 Poster papers, a paper attempts to address the challenges faced when learning from pixel-level inputs in multi-object manipulation tasks. How many different tasks are considered in Figure 3?


Question: How does LED-GFN modify the Detailed Balance loss function?

L_{LED}(s,s') = \left(\log \tilde{F}(s) + \log P_F(s'|s) + \phi_\theta(s\rightarrow s') - \log \tilde{F}(s') - \log P_B(s|s')\right)^2,

Question: How does LED-GFN modify the Detailed Balance loss function?


Question: In ICLR 2024 Oral papers, a paper attempts to solve the problem of how to accelerate the learning process and avoid getting trapped in local optimal solutions in Cooperative Multi-Agent Reinforcement Learning (MARL). This paper develops the deterministic conditional autoencoder, tell me the corresponding loss function.

Your answer should be formula in the Latex format.

Question: In ICLR 2024 Oral papers, a paper attempts to solve the problem of how to accelerate the learning process and avoid getting trapped in local optimal solutions in Cooperative Multi-Agent Reinforcement Learning (MARL). This paper develops the deterministic conditional autoencoder, tell me the corresponding loss function.

\left(H_t - f_{\psi}^H\left(f_{\phi}(s_t | t) | t\right)\right)^2 + \lambda_{rcon} \left\lVert s_t - f_{\psi}^s\left(f_{\phi}(s_t | t) | t\right) \right
Vert_2^2


Question: In ICLR 2024 Poster papers, a paper attempts to solve the credit assignment problem in Preference-based Reinforcement Learning (PbRL). Tell me the number ofpages of the appendix of this paper?


Question: In MetaLA, what's the main improvement for the hidden state $S_t^h$?

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

\mathbf{S}_{t}^{h}=\operatorname{diag}\left(\alpha_{t}^{h}\right) \mathbf{S}_{t-1}^{h}+\left(\mathbf{1}-\alpha_{t}^{h}\right)^{\top} \mathbf{v}_{t} \quad \in \mathcal{R}^{d_{k}^{\prime} \times d_{v}^{\prime}}

Question: In MetaLA, what's the main improvement for the hidden state $S_t^h$?


Question: There is a paper that introduces a novel framework for evaluating language model (LM) agency through structured negotiation games, addressing the limitations of static benchmarks. The work provides an open-source library (LAMEN) and negotiation transcripts to facilitate reproducible research on LM agent capabilities. The authors discuss four types of negotiation issues, among which is the issue where agents value each issue differently, creating opportunities for trade-offs.

Your answer should be one of the four types of negotiation issues: ['Distributive', 'Compatible', 'Mixture', 'Integrative Distributive'].


Question: Among the oral presentations at ICLR 2024 researching image generation, what Frechet Inception Distance (FID) score does Wurstchen Stage C achieve on the COCO30K dataset at 256x256 resolution?

Your answer should be a Python float value representing the FID score, rounded to 1 decimal place.


Question: In ICLR 2024 Poster papers, which paper proposes sub-trajectory mining to extract potentially valuable sub-trajectories from offline data, and diversify the behaviors within those sub-trajectories by varying coverage of the state-action space?

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

Question: In ICLR 2024 Poster papers, which paper proposes sub-trajectory mining to extract potentially valuable sub-trajectories from offline data, and diversify the behaviors within those sub-trajectories by varying coverage of the state-action space.

On Trajectory Augmentations for Off-Policy Evaluation


Question: What are the three steps of the iterative hypothesis refinement process shown in Figure 1? What is the role of the first step, Hypotheses Generation?

Your answer should be a sentence answering the two questions.

The three steps are Hypotheses Generation, Hypotheses Selection, and Hypotheses Refinement

the first step uses LMs to propose a set of free-form or constrained hypotheses based on observations

Question: What are the three steps of the iterative hypothesis refinement process shown in Figure 1? What is the role of the first step, Hypotheses Generation?


Question: At what memory reduction percentage can M-SMoE retain performance on which datasets?

Your answer should be a Python list of two elements, the first is an integer that indicates the percentage, and the second is a list of strings that indicates the dataset names.


Question: A paper propose the Factorized Fourier Neural Operator (F-FNO) which bridges the performance gap between pure machine learning approaches to that of the best numerical or hybrid solvers. In the example where both F-FNO and DNS are using the same spatial resolution, which model is visually closer to the ground truth?

Your answer should be chosen between "F-FNO" and "DNS".


Question: There is a paper that proposes a novel uncertainty-based gradient matching approach for model merging, linking the inaccuracy of parameter averaging to gradient mismatches between individual models and the target model. It employs a new second-order approximation method using Hessian estimates to reduce mismatch. When exploring the effectiveness of removing data, the authors tested the model's toxicity on Detoxify, considering generations with a score exceeding a certain threshold as toxic. What is this specific threshold?

Your answer should be a python float number, rounded to 1 decimal place.


Question: A paper studies the generalized linear contextual bandit problem within the constraints of limited adaptivity and proposes two algorithms, B-GLinCB and RS-GLinCB. In the comparison of RS-GLinCB against ECOLog Faury et al. (2022) and GLOC Jun et al. (2017), which algorithm showed the smallest regret after 17500 rounds?

Your answer should be string, a name of algorithm.


Question: Which Tiny Paper published in ICLR 2024 applies an image-to-image refinement of each image in the InstructPix2Pix dataset with the help of the text-to-image diffusion model SDXL?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Which Tiny Paper published in ICLR 2024 applies an image-to-image refinement of each image in the InstructPix2Pix dataset with the help of the text-to-image diffusion model SDXL?

Improving Image Editing Models with Generative Data Refinement


Question: In all experiments, on average, how many minutes did each model take to train and evaluate?

Your answer should be a float, rounded to 1 decimal place, the time taken for training and evaluating in minutes.


Question: In the paper "HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling," the authors evaluate HLLM on a dataset called PixelRec. Specifically, how many samples within the 200K subset of this dataset have a Session Length in the range of [10, 20]? Please provide the exact number.


Question: In the paper that introduces MuMA-ToM, how many multi-modal social interactions between two agents does the MuMA-ToM Benchmark consist, and how many multi-choice questions are there based on these social interactions?

Your answer should be a Python list of two integers, the first indicating the number of multi-modal social interactions and the second is the number of multi-choice questions.


Question: A paper introduce a novel Contrastive Signal Generative Framework for Accurate Graph Learning to avoid impact of inappropriate contrastive signals. In their model, the hyperparameter $\gamma $ is used to adjust the weight of the contrastive loss in the overall loss function. When $\gamma$ equals to which value, the model achieves the best average accuracy on the three datasets?

Your answer should be a float rounded to 1 decimal place.


Question: How is Q_{EC}(f_{\phi}(s_t), \bm{a}_t) defined in terms of immediate reward r_t and the highest return H from episodic memory? How does the loss function L^{EC}_{\theta} combine the TD error and the episodic memory error?

Your answer should be two formulas in LaTeX format.

L^{EC}_{\theta} = \langle y(s, \bm{a}) - Q_{tot}(s, \bm{a}; \theta)\rangle^2 + \lambda\langle Q_{EC}(f_{\phi}(s), \bm{a}) - Q_{tot}(s, \bm{a}; \theta)\rangle^2

Question: How is Q_{EC}(f_{\phi}(s_t), \bm{a}_t) defined in terms of immediate reward r_t and the highest return H from episodic memory? How does the loss function L^{EC}_{\theta} combine the TD error and the episodic memory error?


Question: In the paper proposing RAGraph, what measures were adopted to emphasize nodes in the long-tail part?

Question: In the paper proposing RAGraph, what measures were adopted to emphasize nodes in the long-tail part? Please answer in a python string format.

Inverse Importance Sampling Strategy. I(v) = \alpha PR(v)+(1-\alpha )DC(v). We reverse the node importance with I'(v) = \frac{1}{I(v)+\epsilon }, \epsilon \to 0, normalize it to obtain node v_i's sampling probabilities pi, and perform weighted sampling function WEIGHTEDSAMPLING(G^R_\tau , p_i) to prioritize nodes with higher sampling probability (lower importance) according to p_i. When sampling, for each master node v_m, we generate its k-hop neighbors, termed an ego net G^e_\tau(v_m).


Question: Recently, an enhanced version of the MMLU dataset, called MMLU-Pro, has been proposed as a benchmark for evaluating large language models (LLMs) by addressing the limitations of the original MMLU dataset. Could you please tell me which discipline in MMLU-Pro has the highest proportion of questions?

Your answer should be a name of a discipline.


Question: What's the main suggestion of the paper inspiring the research of "A KERNEL-BASED VIEW OF LANGUAGE MODEL FINE-TUNING"?

Your answer should be a short text about the main suggestion of the paper.

The findings suggest that random matrix theory, rather than just being  a toy model, may be central to understanding the  properties of neural representations in practice.

Question: What is the main suggestion of the paper?


Question: A paper studies a realistic Continual Learning (CL) setting and applies it to large-scale semi-supervised Continual Learning scenarios with sparse label rate. In the pseudo-supervised class-wise contrastive learning and the instance-wise conrastive learning, which representation distinct the samples into small, seperated clusters but confuses categories?

Your answer should be a string, a name of representation.


Question: A paper introduce DIAMOND (DIffusion As a Model Of eNvironment Dreams) for image generation with visual details. In the comparison with IRIS, IRIS generates the trajectories which contain visual inconsistencies between frames, what is taken as an example to explain probablely consequences for reinforcement learning of this behavior?

Your answer should be a phrase, which conclude the example.

Since an agent should generally target rewards and avoid enemies, these small visual discrepancies can make it more challenging to learn an optimal policy.

Question: A paper introduce DIAMOND (DIffusion As a Model Of eNvironment Dreams) for image generation with visual details. In the comparison with IRIS, IRIS generates the trajectories which contain visual inconsistencies between frames, what is taken as an example to explain probablely consequences for reinforcement learning of this behavior?


Question: In Figure 1, what does the autoregressive model predict for the query Canada in the country-capital example? How does the autoregressive prediction mechanism for the Boolean function task  mirror the country-capital example , despite differing data types?

Your answer should be a sentence answering the two questions.

The model predicts Ottawa as the capital of Canada, completing the sequence

Both tasks require the model to predict the next token (y_k) given previous input-output pairs (x_1,y_1, ..., x_{k-1},y_{k-1}, x_k). The Boolean task predicts a binary label , analogous to predicting a word Ottawa, showcasing the same autoregressive structure but with different data formats.

Question: In Figure 1, what does the autoregressive model predict for the query Canada in the country-capital example? How does the autoregressive prediction mechanism for the Boolean function task  mirror the country-capital example , despite differing data types?


Question: In the paper that proposes L-GATr, where can I find the author's implementation of L-GATr?

Your answer should be a Python strings of the website, the website URL starting with "https://", as given in the paper.


Question: In ICLR 2024 Spotlight papers, which paper is motivated by the idea of "sensing scaffold"?

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

Question: In ICLR 2024 Spotlight papers, which paper is motivated by the idea of "sensing scaffold"?

Privileged Sensing Scaffolds Reinforcement Learning


Question: In the paper that proposes Trap-MID, what percentage is the Recall of the reconstructed images from PLG-MI and what does this statistic mean?

Your answer should be a Python list of two elements. The first is a float number of the percentage (between 0 and 100, rounded to 2 decimal places) and the second is the indication of the statistic.

The MI attacks are misled into extracting trapdoor information.

Question: What percentage is the Recall of the reconstructed images from PLG-MI and what does this statistic mean?


Question: In the paper that presents PANORAMIA, which type of PANORAMIA in ResNet-101 model has the highest precision on CIFAR10 dataset in figure 3 at different levels of recall?

Your answer should be a python strings indicating the variant name of the PANORAMIA.


Question: There is a paper that proposes a multimodal Emotion Recognition and Classification (ERC) framework that leverages bidirectional multi-head cross-attention to model complex correlations across textual, audio, and visual modalities. It introduces a novel visual feature extractor (VisExtNet) for capturing emotion-rich facial expressions and a Sample-Weighted Focal Contrastive (SWFC) loss to address class imbalance and semantic similarity between emotions. The paper employs a dataset called IEMOCAP for emotion classification. Could you please tell me which emotion label category has the second largest sample proportion in this dataset?

Your answer should be a exact match of the emotion label category name.


Question: A recent paper proposes an embodied social intelligence benchmark focusing on accessibility and inclusivity. The benchmark evaluates agents on their ability to infer human intentions and constraints through egocentric observations and to cooperatively plan actions. Please retrieve the paper and provide me with the link to the corresponding code repository for this work.

Your answer should be a link only without any additional prefixes or suffixes.


Question: Can you recommend me a paper published in ICLR 2024 that introduced Hierarchical cOntext MERging (HOMER), a novel method that efficiently addresses the context limit issue inherent in large language models?

Your answer MUST be the pure title of the paper WITHOUT ANY EXPLANATION.

Question: Can you recommend me a paper published in ICLR 2024 that introduced Hierarchical cOntext MERging (HOMER), a novel method that efficiently addresses the context limit issue inherent in large language models?

Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs


Question: A recent paper introduces a proposal-based framework for natural language video localization that enables efficient and effective moment-to-moment interaction through learnable templates and dynamic anchors. By incorporating a multi-scale visual-linguistic encoder and an anchor-guided moment decoder with anchor highlight attention, the proposed method transcends the locality assumption and achieves state-of-the-art performance across several benchmarks. All the research institutions involved in this study are from the same country. Please indicate the name of this country.

Your answer should be a name of a country.


Question: In the paper "TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding," a strategy known as StreamingLLM is employed, which utilizes a KV Cache Eviction Strategy by retaining critical attention sink tokens alongside recent KV pairs to enhance long-context capabilities. The question arises: how many attention sink tokens do the authors of the original StreamingLLM paper consider to be sufficient?


Question: In ICLR 2024 Spotlight papers, a paper introduces a novel algorithm, Robust Policy Improvement (RPI), which actively interleaves between IL and RL based on an online estimate of their performance. What is the formula of $	extbf{A}^+$ advantage function?

Your answer should be the formula in LaTeX format.

Question: In ICLR 2024 Spotlight papers, a paper introduces a novel algorithm, Robust Policy Improvement (RPI), which actively interleaves between IL and RL based on an online estimate of their performance. What is the formula of $	extbf{A}^+$ advantage function?

r(s, a) + \mathbb{E}_{s' \sim \mathcal{P}|\pi, s}\left[f^+(s')\right] - f^+(s)


Question: In the paraphrase category of the experiments in the paper "Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models", which task experienced a decline in accuracy after adding any type of knowledge? What is the significance of this task?

Your answer should be a list containing two elements, the first one is the name of the task being asked, and the second one explains the significance of the task

Existing paraphrase datasets (e.g., Quora Question Pairs) lack challenging negative examples with high lexical overlap but non-paraphrase pairs, and PAWS fills the gap.

Question: What is the significance of PAWS?