Question: In this paper, how many different benchmarks are used?


Question: A recent paper proposes a scalable, low-cost captioning engine, Perceptual Fusion, that integrates specialized vision experts and a multimodal model to generate hyper-detailed image descriptions. Please inform me which subcategory within the Infographics category of the proposed dataset has the largest data volume.

Your answer should be a name of a subcategory.


Question: A recent paper presents MVCN for multimodal sentiment detection, addressing the challenge of modality heterogeneity in text-image pairs. It introduces three novel modules: (1) a Text-Guided Fusion module with Sparse Attention, (2) a Sentiment-based Congruity Constraint, and (3) an Adaptive Loss Calibration strategy. MVCN achieves state-of-the-art results on the MVSA and HFM benchmarks. The research institutions involved in this work are all from the same country. Please provide the name of this country.

Your answer should be a name of a country.


Question: A paper propose Hierarchical Contact Mesh Transformer (HCMT) for modeling complex high-dimensional physical systems. In the experiment of sensitivity to the number of level, generally, with which number of level, the model gets a relatively low RSME?

Your answer should be an int chosen between 1 to 6.


Question: According to the paper at ACL 2023 that studies the projection of retrievers on vocabulary space, by how many percentage points did the strong MPNet model's performance on the BEIR benchmark improve after applying the proposed lexical enrichment at inference time?

Your answer should be a Python list of three floats (between 0 and 100, rounded to 1 decimal place), specifying the percentage point improvement and the before/after values, e.g., [improvement, before, after].


Question: In the experiment of  MULAN with different noise schedule parameterizations, which parameterization yields perform the best?

Your answer should be a string, a kind of parameterizations.


Question: In the paper that identifies and mitigates "conditional image leakage" in I2V-DMs via Analytic-Init and TimeNoise which significantly enhances motion generation, in which domain does the method proposed by the author achieved significant progress compared to the baseline in figure 8? What percentage is the user preference in SVD model when Analytic-Init is used?

Your answer should be a Python list, the first is a string of the name of the domain and the second is a float number rounded to 2 decimal places.


Question: In ICLR 2024 Spotlight papers, a paper proposes a method named "Heuristic Blending". In this paper, the regret in Theorem 1 is bounded by which formula?

Your answer should be the formula in LaTeX format.

Question: In ICLR 2024 Spotlight papers, a paper proposes a method named "Heuristic Blending". In this paper, the regret in Theorem 1 is bounded by which formula?

\min\left(V_{max}, \sqrt{\frac{V_{max}^2(1 - \gamma)|\mathcal{S}|}{N(1 - \gamma(1 - \lambda))^4}}\left(\sqrt{\max_{s,a}\frac{d^{\pi^*}(s,a)}{\mu(s,a)}} + \frac{\gamma\lambda}{1 - \gamma}\sqrt{\max_{(s,a)\in\Omega}\frac{1}{\mu(s,a)}}\right)\right)


Question: What is the core technological breakthrough that enabled R2I to achieve superhuman performance in the Memory Maze task? How does its "acyclic representation model" design enhance long-term memory?

Your answer should be two sentences, eaching answer one of the two questions.

R2I's breakthrough performance in Memory Maze stems from a triple technical synergy. First, the state decoupling architecture separates the deterministic state $h_t$ (generated by SSMs), which captures the trajectory temporal patterns through multi-layer SSM stacking, from the stochastic state $z_t$ (extracted by the CNN encoder), which focuses on preserving the detailed features of visual observation. Second, the KL balancing mechanism dynamically adjusts the scatter weights of the representation model $q_\theta(z_t \mid o_t)$ and the dynamical model $p_\theta(z_t \mid h_t)$, and experiments show that this design stabilizes the KL value in the interval of 0.8-1.2, which effectively prevents the a posteriori collapse. The most crucial innovation is the acyclic representation model: by removing the historical dependence of $q_\theta$ on $h_t$, the generation of $z_t$ is entirely based on the current observation, which not only parallelizes the encoding of latent states, but also reduces the encoding error of key locations by 37% in tasks such as Memory Maze, which requires accurate recall of early observations.

This design breaks the serial computational chain of traditional recursive models by allowing the model to rapidly reconstruct up to 500 steps of historical context during the imagination phase. Together with the multi-scale memory fusion capabilities of SSMs (which enable interaction between underlying details and higher-level semantics through GLU gating), R2I ultimately achieves a 178% outperformance of human players in 3D maze navigation.

Question: What is the core technological breakthrough that enabled R2I to achieve superhuman performance in the Memory Maze task? How does its "acyclic representation model" design enhance long-term memory?


Question: Among the papers at ICLR 2024 researching causal inference, what is the key theoretical result proposed in "Robust agents learn causal world models" regarding agents' ability to generalize under distributional shifts?

Your answer should be a Python string describing the key theoretical finding about the relationship between robust agents and causal models.

The paper proves that any agent capable of achieving low regret across a wide range of distributional shifts must have learned an approximate causal model of the environment, which converges to the true causal model for optimal agents.

Question: What is the key theoretical result proposed in "Robust agents learn causal world models" regarding agents' ability to generalize under distributional shifts?


Question: A recent paper proposes Conditional Mutual Information for Disentanglement (CMID), a method for learning disentangled representations in reinforcement learning with correlated features by minimizing conditional mutual information, guided by the causal structure of the Markov Decision Process. This approach improves generalization under correlation shifts and outperforms existing methods on continuous control tasks. Question: On which tasks or datasets was the effectiveness of this method evaluated?

Your answer should be the name of a dataset.

the DeepMind Control Suit
DeepMind Control Suit
DMC


Question: The UNet-FNO trained on a small dataset (1000 data pairs) achieves better performance compared to other neural operator architectures trained on a significantly larger dataset (10000 data pairs). What does it suggests?

UNetFNO is particularly advantageous in scenarios with limited training data, where it can achieve competitive accuracy while significantly reducing the computational cost and time associated with data generation.

Question: The UNet-FNO trained on a small dataset (1000 data pairs) achieves better performance compared to other neural operator architectures trained on a significantly larger dataset (10000 data pairs). What does it suggests?


Question: What is the core idea of G-SHELL and what is its application?

Your answer should be a sentence answering the question.

Inspired by the observation that open surfaces can be seen as islands floating on watertight surfaces, we parameterize open surfaces by defining a manifold signed distance field on watertight templates. With this parameterization, we further develop a grid-based and differentiable representation that parameterizes both watertight and non-watertight meshes of arbitrary topology. Our new representation, called Ghost-on-the-Shell (G-SHELL), enables two important applications: differentiable rasterization-based reconstruction from multiview images and generative modelling of non-watertight meshes.

Question: What is the core idea of G-SHELL and what is its application?


Question: What certified accuracy does GNNCert achieve on the MUTAG dataset when an attacker arbitrarily adds or deletes one edge?

Your answer should be a Python integer representing the percentage of certified accuracy achieved on the MUTAG dataset.


Question: In ICLR 2024 Poster papers, a paper attempts to solve how to effectively learn from off-policy data in reinforcement learning. Tell me the affiliation of the first author.

Question: In ICLR 2024 Poster papers, a paper attempts to solve how to effectively learn from off-policy data in reinforcement learning. Tell me the affiliation of the first author.

Max Planck Institute for Intelligent Systems


Question: There is a recent paper that proposes a novel contrastive learning framework for multimodal sentiment analysis, which uniquely combines intra-sample feature decomposition and inter-sample contrastive learning. Each modality (text, vision, audio) is decomposed into similarity and dissimilarity features, with text-based similarity features used as anchors for contrastive alignment. I would like to know, on which dataset was the primary experiment of this work conducted?

Your answer should include only the single most significant dataset.


Question: What is the size and annotation detail of the AbdomenAtlas 1.1 dataset?

Your answer should be a sentence describing the size and annotation detail of the AbdomenAtlas 1.1 dataset, including the number of CT volumes and the types of annotations provided.

The AbdomenAtlas 1.1 dataset comprises 9,262 three-dimensional CT volumes with high-quality, per-voxel annotations of 25 anatomical structures and pseudo annotations of seven tumor types.

Question: What is the size and annotation detail of the AbdomenAtlas 1.1 dataset?


Question: In the paper that proposes proposing a theoretically-grounded plug-and-play module that enables efficient and accurate structure learning with minimal data requirements, what is the use and the caracterization of function h(B) in formula (4)?

Your answer should be a Python strings indicating the use and catacterization of h(B).

h(B) is the acyclicity of the graph and it can be characterized by a series of continuous functions for a non-negative matrix B.

Question: In the paper that proposes proposing a theoretically-grounded plug-and-play module that enables efficient and accurate structure learning with minimal data requirements, what is the use and the caracterization of function h(B) in formula (4)?


Question: In ICLR 2024 Spotlight papers, a paper attempts to alleviate the over-optimization problem that occurs when LLMs are optimized by reward models through human feedback. What is the affiliation of the corresponding author?


Question: In ICLR 2024 Spotlight papers, which paper introduces a novel algorithm, Robust Policy Improvement (RPI), which actively interleaves between IL and RL based on an online estimate of their performance?

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

Question: In ICLR 2024 Spotlight papers, which paper introduces a novel algorithm, Robust Policy Improvement (RPI), which actively interleaves between IL and RL based on an online estimate of their performance?

Blending Imitation and Reinforcement Learning for Robust Policy Improvement


Question: A recent paper proposes a unified benchmark framework for evaluating task-agnostic decoupling methods in privacy-preserving machine learning using synthetic image generation. It systematically integrates adversarial representation learning techniques into a synthetic data pipeline based on latent diffusion models (LDMs) and introduces standardized evaluation protocols for both privacy and utility. Could you please tell me which university the first author of this work is affiliated with?

Your answer should be a name of a university.


Question: In the paper "ZeroStance: Leveraging ChatGPT for Open-Domain Stance Detection via Dataset Generation," how many baseline human-annotated datasets are incorporated?


Question: In the paper introducing program-based reasoning, which benchmarks were used to evaluate performance on mathematical problems?

Your answer should be a Python list of strings.


Question: In ICLR 2024 Spotlight papers, which paper unifies reinforcement learning and imitation learning methods under a dual framework?

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

Question: In ICLR 2024 Spotlight papers, which paper unifies reinforcement learning and imitation learning methods under a dual framework?

Dual RL: Unification and New Methods for Reinforcement and Imitation Learning


Question: According to the paper that proposes Wanda, in what aspects does this pruning approach differ from SparseGPT?

Your answer should be a plain text that describes the differences between Wanda and SparseGPT.

Wanda entails no weight update on pruned networks, uses different pruning metric that computes faster than SparseGPT, and has a lower algorithm complexity.

Question: According to the paper that proposes Wanda, in what aspects does this pruning approach differ from SparseGPT?


Question: In ICLR 2024 Spotlight papers, a paper introduces a new conception named "Effective Horizon". In this paper, what is the formula of SQIRL sample complexity?

Your answer should be the formula in LaTeX format.

Question: In ICLR 2024 Spotlight papers, a paper introduces a new conception named "Effective Horizon". In this paper, what is the formula of SQIRL sample complexity?

\widetilde{O}\left(kT^3\alpha^{2(k - 1)}A^{\bar{H}_k}D\log(\alpha D)/\epsilon\right)


Question: There is a recent paper that proposes a method called CCPA, a two-stage debiasing framework that combines continuous prompt augmentation and contrastive learning to mitigate social biases, particularly gender bias, in pre-trained language models. The authors validated the effectiveness of CCPA on the Bias-in-Bios dataset. Please answer: By how many percentage points does the Accuracy (all) metric of the model using CCPA improve compared to the original BERT model in absolute terms?

Your answer should be a Python float, rounded to two decimal places.


Question: Why did the authors use newly-published books as the database of BOOOOKSCORE?

To reduce the confounding impact of summary memorization, we manually collect 100 books3 published within the past year to form our dataset. Some of these books could still have appeared in the pretraining dataset of recent LLMs such as Claude 2 and LLaMa2, although it is much less likely than in BookSum. However, summaries of these books do not publicly exist: we did not find summaries online for any books in our dataset, which significantly lowers the possibility of LLM memorization.

Question: Why did the authors use newly-published books as the database of BOOOOKSCORE?


Question: A paper propose deleting edges to address over-squashing and over-smoothing of Message Passing Graph Neural Networks simultaneously. In their examination of effectiveness of the proposed edge modification algorithms in the spectral gap expansion, what makes their ideal baseline computationally too expensive?

Your answer should be a phrase which gives the reason.

Each edge scoring requires O(|E|) computations.

Question: A paper propose deleting edges to address over-squashing and over-smoothing of Message Passing Graph Neural Networks simultaneously. In their examination of effectiveness of the proposed edge modification algorithms in the spectral gap expansion, what makes their ideal baseline computationally too expensive?


Question: Which theorem is used to find the solution of formula (1). Why the linear approximator cannot be applied directly when non-negativity constraints are imposed on target function f \geq 0?

Your answer should be list of two elements, the first is the name of the theorem and the second is the reason why the linear approximator cannot be applied. Note that you should output the formula in the LaTex format.

Because the linear approximator generally has negative values, even under non-negative kernel k(\cdot, \cdot) \geq 0.

Question: Which theorem is used to find the solution of formula (1). Why the linear approximator cannot be applied directly when non-negativity constraints are imposed on target function f \geq 0?


Question: In the paper that combines within-lifetime extrinsic learning and cross-lifetime intrinsic motivation in a single framework, three different behaviours are evaluated in figure 1. Which behavious's intrinsic motivation of most frequent choice is always lower than that of the sum of other choices?

Your answer should be a python strings of the description of the behaviour.

Behavior on the stationary 10-armed bandit task.

Question: In the paper that combines within-lifetime extrinsic learning and cross-lifetime intrinsic motivation in a single framework, three different behaviours are evaluated in figure 1. Which behavious's intrinsic motivation of most frequent choice is always lower than that of the sum of other choices?


Question: Among the text-to-SQL papers in ACL 2023, which one achieves the best testsuite accuracy on the SPIDER dataset? Tell me the paper title and corresponding test accuracy.

Your answer should be a Python list of length two, with the first one being the title string and the second one being a float, the accuracy rounded to 3 decimals.

G3R: A Graph-Guided Generate-and-Rerank Framework for Complex and Cross-domain Text-to-SQL Generation

Question: Among the text-to-SQL papers in ACL 2023, which one achieves the best testsuite accuracy on the SPIDER dataset? Tell me the paper title.


Question: In ICLR 2024 Poster papers, a paper tackles the Offline Opponent Modeling problem by harnessing the full potential of the supervised pre-trained Transformers' in-context learning capabilities. Tell me the affiliation of the corresponding author.


Question: In the paper proposing MC-DiT, why does Gaussian noise affect the optimization process of L_{asym}?

Your answer should be a python strings with specific formula items.

Question: In the paper proposing MC-DiT, why does Gaussian noise affect the optimization process of L_{asym}?

Because Gaussian noise introduces two noise-weighted items representing contrastive objective between h(x^1_t ) and [\frac{\partial h_g}{\partial x^2_0}] (h_g (x^2_0) and [\frac{\partial h}{\partial x^1_0} ]). As the formula (7) shows, L_{\text{asym}-NN} = -\mathbb{E}_{p(x_t^1, x_t^2)}\left[h(x_t^1)^T h_g(x_t^2)\right] \approx L_{\text{asym}} + \mathbb{E}\left(-h(x_t^1)^T \left[rac{\partial h_g}{\partial x_0^2}\right]^T n\right) + \mathbb{E}\left(-h_g(x_0^2)^T \left[\frac{\partial h}{\partial x_0^1}\right]^T n\right).


Question: In ICLR 2024 Spotlight papers, which paper shows that the solution of this entropy-regularized problem corresponds to a Quantal Response Equilibrium (QRE), a generalization of Nash equilibria that accounts for bounded rationality?

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

Question: In ICLR 2024 Spotlight papers, which paper shows that the solution of this entropy-regularized problem corresponds to a Quantal Response Equilibrium (QRE), a generalization of Nash equilibria that accounts for bounded rationality?

Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula


Question: In the paper that introduces PEACE dataset, where can I find the dataset? In which conference was this paper published?

Your answer should be a Python list of two strings. The first is a website URL starting with "https://", as given in the paper, the second is only the conference name string.

38th Conference on Neural Information Processing Systems (NeurIPS 2024)

Question: In the paper that introduces PEACE dataset, where can I find the dataset? In which conference was this paper published?


Question: There is a novel framework for end-to-end task-oriented dialog systems that decouples knowledge retrieval from response generation, named MAKER (Multi-grAined Knowledge Retriever). The implemented Knowledge Retriever consists of two selectors, one of which is the 'Entity Selector.' What is the name of the other selector?

Your answer should be the exact name of the other selector.


Question: On the LSUN bedroom dataset in the paper "GENERALIZATION IN DIFFUSION MODELS ARISES FROM GEOMETRY-ADAPTIVE HARMONIC REPRESENTATIONS", when N=100, the cosine similarity of the generated samples to the nearest neighbors of the training set higher than how much will be considered memory overfit) ?

Your answer should be a float, rounded to 1 decimal place, evaluating in percentage.


Question: According to the paper at ACL 2023 that introduces MisGendered, what is the average accuracy of these models in predicting gender-neutral pronouns without fine-tuning?

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


Question: How does the $\epsilon$ parameter of AdamW affect training?

Your answer should be a sentence, discussing how the relationship between $\epsilon$ and grad RMS impacts training.

If the grad RMS is on the same order as $\epsilon$, then $\Delta$ will decrease in magnitude. Decreasing $\epsilon$ to 1e-15 improves loss and mitigates a collapse in grad RMS.

Question: How does the $\epsilon$ parameter of AdamW affect training?


Question: A paper negates the invariable outperformance of Cross-Validation (CV) in the simple "plug-in" approach in terms of both the asymptotic bias and coverage accuracy. In their study, 2- and 5-fold CVs suffer from larger biases than plug-in, this phenomenon is more evident under small or large sample sizes?

Your answer should be chosen between "small" and "large".


Question: In ICLR 2024 Poster papers, a paper proposes a framework named LaMo (Language Models for Motion Control). How many baselines are compared in Figure 1?


Question: A recent paper introduces MassSpecGym, the first comprehensive benchmark for molecule discovery and identification from tandem mass spectrometry (MS/MS) data. Could you please retrieve the article and specify the exact number of dataset entries that contain normalized collision energies?


Question: According to this paper, which dataset(except the dataset proposed by this paper) also includes controls for unary vs binary predicates among the existing datasets for the formal analysis of reasoning ability? Can you describe the structure of each example in it in detail?

Your answer should be a single python list like this: ["dataset_name", "structure_description"]. Note that for the dataset name, the abbreviation is required. For the structure description, you should give a short string to describe the structure.

Each example in SimpleLogic is a proposi tional reasoning problem that only involves def inite clauses. In particular, each example is a tuple  (facts, rules, query, label) where (1) facts is a list  of predicates that are known to be True, (2) rules  is a list of rules represented as definite clauses, (3)  query is a single predicate, and (4) label is either  True or False, denoting whether the query predicate  can be proved from facts and rules.

Question: What is the structure of each example in the dataset?


Question: In the paper that proposes VATT, along which dimension are E_lm and E_a^M concatenated to obtain the fused features \[E_{mm} = \text{Concat}(\left[ E_{lm}, E_{a}^{M} \right])\]

Your answer should be a Python strings precising the dimension.

Question: In the paper that proposes VATT, along which dimension are E_lm and E_a^M concatenated to obtain the fused features \[E_{mm} = \text{Concat}(\left[ E_{lm}, E_{a}^{M} \right])\]


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. How many different tasks are considered in Figure 2?


Question: On the DeepCoder dataset, how much did ExeDec improve the average accuracy of the combined generalization task compared to the Transformer baseline?

Your answer should be a float, rounded to 1 decimal place, evaluating in percentage.


Question: Which dataset(s) is ViewCo trained on? I want to get the newer dataset, can you give me the link to it?

Your answer should be s single python list like this: [["dataset_name1","dataset_name2"], "https://github.com/a/b"]. Note that for the dataset name, the abbreviation is required.


Question: There is a paper that introduces Codable Text Watermarking for Large Language Models (CTWL), a novel framework for embedding multi-bit customizable information into LLM-generated texts. Its key contribution is a method called Balance-Marking, which leverages a proxy language model to partition the vocabulary into probability-balanced subsets. In the watermarking process of its experiments, the total time expenditure can be divided into Encoding Time and Decoding Time. Which part consumes more time?

Your answer should be one of ['Encoding Time', 'Decoding Time']


Question: In the paper that demonstrates LLM-generated difficulty labels can outperform human labels in curriculum learning for fine-tuning, how much percentage does the accuracy gains of learning strategy with LLM-defined difficulty outperform that with human-defined difficulty on average by datasets, compared to the Random Shuffle baseline?

Your answer should be a float number, rounded to 2 decimal place.


Question: In the paper that introduces PPDPP, a plug-and-play dialogue policy planner for LLM-powered proactive dialogue agents, the author proposed a self-play framework utilizing LLM-based user simulation and reward modeling for interactive training. In the experiments related to training episodes, which model consistently maintains the highest Success Rate on the CraigslistBargain dataset?

Your answer should be the exact name of a model.


Question: In the paper that models novelty emergence in science as an evolutionary game and reveals agents with selfish strategies maximise the diversity of novel ideas, in which generation has the highest Average Novelty Score? What does the image of the Average Novelty Score Time Evolution suggest?

Your answer should be a Python list. The first is an interger of the number of the generation and second is a Python strings of the result suggested by the image.

The novelty of ideas may decline when collective decision-making is involved, compared to when individuals generate ideas based solely on their own thoughts.

Question: In the paper that models novelty emergence in science as an evolutionary game and reveals agents with selfish strategies maximise the diversity of novel ideas, in which generation has the highest Average Novelty Score? What does the image of the Average Novelty Score Time Evolution suggest?


Question: In the paper titled "Can Whisper Perform Speech-Based In-Context Learning?" the experimental section utilized two types of Chinese dialects, one being Chongqing. What is the other one?

Your answer should be the specific name of the dialect without any additional prefixes or suffixes.


Question: Recent studies use the method of decode the linguistic intricacies to decode DNA. A paper propose to replace k-mer tokenization with Byte Pair Encoding (BPE). They shows model performance averaged over each tasks (macro) and individual dataset (micro), in which kind of performance, macro or micro, the model loss about 2.5% of its performance point when the vocabulary changes from a middle size to a considerable size?

Your answer should be chosen between "macro" and "micro".


Question: In the paper that introdeces GRIT, whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions, which generative model in the Figure 1 behaves best in embedding perfornmance?

Your answer MUST be ONE single string without explanation of the method's name in abbreviation and its number of parameters in billions (B). For example, your answer could be 'LLaMA 2 70B'.


Question: In the experiment of real-world data in the paper of "CALIBRATION MATTERS: TACKLING MAXIMIZATION BIAS IN LARGE-SCALE ADVERTISING RECOMMENDATION SYSTEMS", which base model was used? What's the architecture of this base model?

Your answer should be a single python list like this: ["model_name", "model_architecture"]. Note that for the model name, the abbreviation is required. For the model architecture, you should give a string to describe the architecture.

Input Features: DLRM takes both categorical features and continuous features as input.
Embeddings: Categorical features are converted into dense vectors using embeddings. Each category is represented by an embedding vector.
Bottom MLP: Continuous features are processed by a multilayer perceptron (MLP) called the 'bottom MLP'.
Feature Interaction: The output embedding vectors and the output of the bottom MLP are then interacted explicitly using dot products between all pairs of vectors. These interactions capture second-order feature interactions.
Top MLP: The interacted features are concatenated with the original bottom MLP output and fed into another MLP called the 'top MLP' for further processing.
Output Layer: The output of the top MLP is then passed through a sigmoid function to produce a probability prediction (e.g. probability of a click).

Question: What is the architecture of the model?


Question: In ICLR 2024 Poster papers, a paper attempts to solve how to effectively learn from off-policy data in reinforcement learning. The authors claim that their components can be learned jointly by maximizing the conditional evidence lower bound (ELBO), what is its formula?

Your answer should be the formula in Latex format.

Question: In ICLR 2024 Poster papers, a paper attempts to solve how to effectively learn from off-policy data in reinforcement learning. The authors claim that their components can be learned jointly by maximizing the conditional evidence lower bound (ELBO), what is its formula?

- D_{\text{KL}}\left(q_{\tilde{\phi}}(z | s, a, s') \| p_{\tilde{\theta}}(z | s, a)\right) + \mathbb{E}_{z \sim q_{\tilde{\phi}}(\cdot | s, a, s')} \left[ \log p_{\tilde{\theta}}(s' | s, a, z) \right]


Question: In ICLR 2024 Poster papers, a paper attempts to conduct long-range dynamic modeling in an interactive environment. In this paper, why the authors introduce the Koopman operator?

Question: In ICLR 2024 Poster papers, a paper attempts to conduct long-range dynamic modeling in an interactive environment. In this paper, why the authors introduce the Koopman operator?

The Koopman operator is a linear operator used to study the dynamics of nonlinear dynamical systems. By mapping the nonlinear dynamical system to a high-dimensional latent space (observable space), the dynamics of the system can be linearized.


Question: A recent paper introduces ReactZyme, a large-scale benchmark dataset and retrieval-based framework that directly models enzyme functions through their catalyzed reactions rather than traditional annotations such as EC or GO. Could you please tell me how many #Molecule/Reaction are included in the proposed dataset?


Question: In this paper, how many different inference frameworks of predictor are proposed?


Question: This paper proposes a method called "DLPA", which kind of planning is used in it?

Question: This paper proposes a method called "DLPA", which kind of planning is used in it?


Question: Is there a paper published in ICLR 2024 which establishes a unified framework for Riemannian Batch Normalization (RBN) techniques on Lie groups?

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

Question: Is there a paper published in ICLR 2024 which establishes a unified framework for Riemannian Batch Normalization (RBN) techniques on Lie groups?

A Lie Group Approach to Riemannian Batch Normalization


Question: According to the paper's experimental results, which genre of model performed best in the book-length summary task?

Your answer should be a sentence, comparing the models according to catogories.

The experimental results show that closed-source models (e.g., GPT-4 and Claude 2) generate summaries with the highest BooookScore, with Claude 2 performing particularly well under the incremental update strategy. Among the open-source models, Mixtral performs close to GPT-3.5-Turbo, while LLaMA 2 performs the worst

Question: Which of the paper's experimental results showed that the model performed best in the book-length summary task?


Question: A recent paper introduces SIMSR, a novel Smart Reply framework that leverages model-based simulation to optimize response set selection by directly maximizing the relevance of at least one reply through a learned Matching model acting as a world simulator. This work is a collaboration between a certain university and Nokia Bell Labs. Please provide the name of this university.

Your answer should be the name of the university.

Nottingham
University of Nottingham
The University of Nottingham


Question: A recent paper introduces a novel temporal knowledge graph embedding model that uniquely maps relation-time pairs onto an Archimedean spiral in complex space. This design transforms the temporal link prediction task into a third-order tensor completion problem, enabling precise modeling of relation dynamics while maintaining time-invariant entity representations. The experiments are conducted on three TKGE datasets. Which of these datasets has the largest number of training samples?

Your answer should be one of the following: ICEWS14, ICEWS05-15, GDELT.


Question: What precision does I2D2 achieve in identifying true commonsense statements compared to GPT-3?

Your answer should be a Python dictionary with keys 'I2D2_precision' and 'GPT3_precision', with values as float numbers rounded to 2 decimal places.


Question: In the paper proposing UNIT, what does the reconstruction loss mean in figure 1?

Question: In the paper proposing UNIT, what does the reconstruction loss mean in figure 1?

The output of the visual decoder are student tokens. The output of the original pretrained ViT model are original features for each natural image. we enforce the alignment of the new learned student tokens with the original ones using a weighted sum of the cosine distance L_{cos} and smooth L1 loss L_{l1}, which is denoted as reconstruction loss.


Question: In the paper that proposes a statistically robust and multi-metric comparison framework for classifiers, in which library can I find the data sets and the performance evaluation of the 80 datasets that the author used to compare to the SVM?

Your answer should be a Python string of the name of the library.


Question: Among the papers in ICLR 2024, which paper proposes the conception called "Policy Rehearsing"? How the authors define "Optimal Policy Gap"?

Your answer should be the formula in LaTeX format.

Question: Among the papers in ICLR 2024, which paper proposes the conception called "Policy Rehearsing"? How the authors define "Optimal Policy Gap"?


Question: What type of input-output pairs are shown in the prompt for in-context learning of Boolean functions? How does the structure of the prompt in the Boolean function task differ from the in-context learning example with country-capital pairs?

Your answer should be a sentence answering the two questions.

The prompt consists of binary input-output pairs

While the Boolean function task uses binary sequences with explicit input-output mappings (e.g., x_i, y_i), the country-capital example uses natural language. The Boolean task is fully discrete, while the latter relies on semantic relationships.

Question: what type of input-output pairs are shown in the prompt for in-context learning of Boolean functions? How does the structure of the prompt in the Boolean function task differ from the in-context learning example with country-capital pairs?


Question: Which categories of KV cache compression techniques within PoD does WindowKV belong to?

Your answer should be a string that represents categories of KV cache compression techniques in PoD.

Token-selection-based methods and Layer-sharing-based methods

Question: Which categories of KV cache compression techniques within PoD does WindowKV belong to?


Question: What is the formula of the cosine similarity distribution of random vectors in a high-dimensional space (in Proposition 3.1)?

Question: What is the formula of the cosine similarity distribution of random vectors in a high-dimensional space


Question: There is a recent paper that investigates the phenomenon of 'overthinking' in pretrained language models (PTMs), specifically within open-world scenarios for out-of-domain (OOD) intent classification tasks. The authors propose a dynamic early-exiting inference method that utilizes ensemble-based internal classifiers to determine whether sufficient confidence has been reached for classifying OOD intents before completing inference. Who is the corresponding author of this work?

Your answer should be a name of a person.


Question: By how many percentage points did BREAK improve the joint goal accuracy on the MultiWOZ 2.1 dataset compared to the previous best-performing models?

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


Question: A recent paper introduces the first large-scale UAV-based dataset explicitly designed for multi-object tracking (MOT) and re-identification (Re-ID) of wild animals, focusing on lekking blackbuck antelopes. The dataset includes over 1.2 million MOT annotations across 12 high-resolution drone videos and 730 Re-ID tracks captured from synchronized UAVs. Could you please provide the email address of one of the corresponding authors?


Question: A paper introduce a novel principled strategy for building an iterative learning algorithm via the optimisation of a sequence of surrogate training objectives. In the experiment, does their model SuPAC-CE converge faster than gradient descent?


Question: In the paper that introduces asymptotically faster and memory-efficient ASQ, a paper in the references approaches the ASQ problem using a dynamic programming approach that allows one to optimize Q in polynomial time. In which conference was this paper published?

Your answer should be a Python strings of the name of the conference.

International Conference on Machne Learning

Question: In the paper that introduces asymptotically faster and memory-efficient ASQ, a paper in the references approaches the ASQ problem using a dynamic programming approach that allows one to optimize Q in polynomial time. In which conference was this paper published?


Question: A paper studies neuronal embeddings' stablility with respect to changes in model architecture and initialization. In their observation of a strong dependency of the neuronal embeddings on the type of readout mechanism. What is probablely a cause of the phenomenon that the structure that emerges for the Gaussian readout, arises trivially by very aggressively forcing weights to be zero?

Your answer should be a phrase, which reveal the reason.

Question: A paper studies neuronal embeddings' stablility with respect to changes in model architecture and initialization. In their observation of a strong dependency of the neuronal embeddings on the type of readout mechanism. What is probablely a cause of the phenomenon that the structure that emerges for the Gaussian readout, arises trivially by very aggressively forcing weights to be zero?


Question: In the paper that proposes ICTM, an algorithm to approximate the MAP solution to a variety of linear inverse problems using a flow prior, what does the x_t in formula (9) represent and how are x_1 and the prior term approximated computationally?

Your answer should be a Python strings indicating the representation of x_t and the method of the approximation.

x_t := x_t(x_0) denotes the intermediate state x_t generated from x_0. x_1 and the prior term can be approximated by an ODE solver: ODESolve(x_0, 0, t, v_\theta), where x_0 is the initial point, and the second and third arguments represent the starting time and the ending time, respectively.

Question: In the paper that proposes ICTM, an algorithm to approximate the MAP solution to a variety of linear inverse problems using a flow prior, what does the x_t in formula (9) represent and how are x_1 and the prior term approximated computationally?


Question: In the paper that proposes QIM-compatibility as a unified framework that extends graphical models to hypergraphs, enabling new capabilities for representing functional dependencies and cyclic causality, what does H_\mu (T_a \mid S_a) in formula (2) represent?

Your answer should be a Python strings of the representation of H_\mu (T_a \mid S_a), the formula in LaTeX format.

H_\mu (T_a \mid S_a) is a conditional entropy that measures how far \mu is from satisfying the functional dependency S_a \rightarrow T_a.

Question: In the paper that proposes QIM-compatibility as a unified framework that extends graphical models to hypergraphs, enabling new capabilities for representing functional dependencies and cyclic causality, what does H_\mu (T_a \mid S_a) in formula (2) represent?


Question: According to the MedCalc-Bench paper, which datasets for LLM involves all five categories: Medical, Knowledge, Qual. Reasoning,Comput., Non-MCQ?

Your answer should be a string, a name of dataset


Question: In the single-object editing results on OBJect unseen object subset, which model gets the highest LPIPS in translation task?

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


Question: A recent paper introduces COFE, a novel benchmark derived from COGS, specifically designed to systematically study in-context compositional generalization in large language models. In the experiments investigating model performance with different levels of structural similarity, at what structural similarity level did code-davinci-002 achieve optimal performance on the PhraReco dataset?

Your answer should be one of the following options: ['Without Structural Similarity', 'Rough Structural Similarity', 'Precise Structural Similarity'].


Question: A recent paper introduces a novel parameter-efficient fine-tuning method for pre-trained language models that selectively fine-tunes only the most informative and correlated attention heads. Specifically, the paper models head relationships through a graph that combines information richness (via SVD) and inter-head correlation, ranking them with the PageRank algorithm. In the experiments investigating the effect of the number of selected heads, on which dataset did the performance show the greatest absolute increase?

Your answer should be one of: ['MRPC', 'MNLI', 'RTE', 'CoLA']


Question: Can you recommend me a paper published in ICLR 2024 that introduces a lightweight schema for enabling machine learning over electronic health record data?

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 introduces a lightweight schema for enabling machine learning over electronic health record data?

Medical Event Data Standard (MEDS): Facilitating Machine Learning for Health


Question: A recent paper introduces a novel large-scale dataset composed of over 159 billion tokens extracted from publicly available business disclosures (e.g., SEC EDGAR filings). It is uniquely characterized by its domain specificity (business and finance), high factuality, low toxicity, and rich temporal metadata. Please provide me with the email address of the corresponding author of this paper.


Question: How is the forward diffusion process defined in the paper?

dxt = f (t) xtdt + g (t) dwt, \quad x0 \sim q0 (x0),

Question: How is the forward diffusion process defined in the paper?


Question: There is a paper that introduces a unified and effective sequence tagging framework for relational structure extraction tasks, such as event argument extraction, relation extraction, and task-oriented semantic parsing. By appending verbalized representations of conditions and relationships to the input text, a method termed priming, the framework leverages pre-trained language models to generate condition- and relation-aware contextual embeddings. The main experiments in this work were conducted on the MTOP dataset. specifically, how many types of languages of data from MTOP were used in the study?


Question: Which paper did Xueyi Liu and Li Yi pubulish in ICLR 2024 poster volume?

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

Question: Which paper did Xueyi Liu and Li Yi pubulish in ICLR 2024 poster volume?

GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion


Question: In the paper that proposes a dynamic agentic framework with task-graph decomposition, introduces domain-specific evaluation metrics, and provides a specialized dataset for analyzing LLM-based autonomous agents, what are the core metrics the author uses to evaluate the accuracy and completeness of tool identification?

Your answer should be a Python list indicating the name of the metrics.


Question: How many percentage of statement-level and category-level tasks are there in the MGToolBench dataset?

Your answer should be an integer between 0 and 100, without a decimal point


Question: In the paper that Seohong Park(the first author) published in the ICLR 2024 oral volume, which method performs second best in Quantitative comparison with unsupervised skill discovery methods?

Your answer MUST be ONE single string of ONE word without explanation of the method's name in abbreviation.


Question: Describe the attention pattern differences in Layer 1 before vs. after the abrupt transition. How does Layer 2's target-specific attention (bottom row) explain the network's post-transition IC accuracy?

Your answer should be a sentence answering the two questions.

Before: Layer 1 shows uniform attention (no structure). After: Queries attend strongly to immediately preceding tokens (diagonal pattern).

Post-transition, the target (query) attends sharply to the correct label's position (highlighted key), directly enabling accurate in-context prediction by copying the label from the demonstrative pair.

Question: Describe the attention pattern differences in Layer 1 before vs. after the abrupt transition. How does Layer 2's target-specific attention (bottom row) explain the network's post-transition IC accuracy?


Question: In the paper that releases Content Behavior Corpus, according to Figure 2, LCBM lags behind on only one perspective. In that perspective, which model has the best performance?

Your answer should be a Python string, the model name as shown in the corresponding figure.


Question: Can you recommend me a paper published in ICLR 2024 that introduces the Dynamic Signal Distribution(DSD) classification task?

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 introduces the Dynamic Signal Distribution(DSD) classification task?

Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs


Question: Can you recommend me a paper published in ICLR 2024 that develops the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of the information bottleneck?

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 develops the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of the information bottleneck?

Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning


Question: Is there a paper published in ICLR 2024 which introduce an end-to-end interpretability framework designed to quantify context usage in language models' generations?

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

Question: Is there a paper published in ICLR 2024 which introduce an end-to-end interpretability framework designed to quantify context usage in language models' generations?

Quantifying the Plausibility of Context Reliance in Neural Machine Translation


Question: What is the core advantage of "Highway policy"?

Question: What is the core advantage of "Highway policy"?

The core advantage of "Highway policy" is that it can avoid learn every navigation trajectory alone, which is time-consuming and inefficient.


Question: In NeurIPS 2024 Poster papers, a paper proposes a methods called "BECAUSE". In this paper, what is the formula of the Theorem 1 (Performance guarantee)?

Your answer should be the formula in LaTeX format.

Question: In NeurIPS 2024 Poster papers, a paper proposes a methods called "BECAUSE". In this paper, what is the formula of the Theorem 1 (Performance guarantee)?

V_1^*(\widetilde{s}) - V_1^{\pi}(\widetilde{s}) \lesssim \min\left\{C_1 \log\left(\frac{\lVert M \rVert_0}{\xi}\right)\sqrt{|\mathcal{S}|}, C_s \sigma \sqrt{\lVert M \rVert_0}\right\}\sum_{h = 1}^{H}\mathbb{E}_{\pi^*}\left[\sqrt{\frac{\log(1 / \delta)}{n(s_h, a_h)}} \, \big\vert\, s_1 = \widetilde{s}\right]


Question: What pruning metric is used by Wanda?

Your answer should be the context that describes the pruning metric used by the paper.

In Wanda, the pruning metric is computed by the product of the linear weight's magnitude and its corresponding input feature norm. Specifically, the score for the linear weight $\mathbf{W}_{ij} is defined by:

\begin{equation}
\mathbf{S}_{ij} = \left|\mathbf{W}_{ij}\right|\cdot\Vert\mathbf{X}_j\Vert_2
\end{equation}

where $\left|\cdot\right|$ represents the absolute value operator, and $\Vert\mathbf{X}_j\Vert_2$ evaluates the L2 norm of $j$th features aggregated accros different tokens.

Question: What pruning metric is used by Wanda?


Question: In ICLR 2024 Poster papers, a paper proposes a framework named LaMo (Language Models for Motion Control). Tell me the affiliation of the first author.


Question: For the MiniARC task examples in Figure 1, what is listed as a Bad Rule? How does this Bad Rule differ from the Good Rule in terms of output when applied to instances?

Your answer should be a sentence answering the two questions.

The Good Rule, Drop all objects, correctly removes objects, while the Bad Rule produces incorrect outputs by swapping colors instead

Question: For the MiniARC task examples in Figure 1, what is listed as a Bad Rule?How does this Bad Rule differ from the Good Rule in terms of output when applied to instances?


Question: A recent paper introduces the SENSORIUM 2023 Benchmark Competition, a large-scale standardized framework for evaluating predictive models of mouse primary visual cortex responses to dynamic visual stimuli. Please retrieve the paper and provide me with the link to the corresponding GitHub code repository for this work.

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


Question: How many papers are published as an oral in ICLR 2024 Workshop on Large Language Model (LLM) Agents?

Your answer should be a single Python integer.


Question: A recent paper analyzes intermediate training checkpoints of OPT language models of varying sizes (ranging from 125M to 175B parameters) across token prediction, sequence-level generation, and downstream tasks. This work is a collaboration between a certain university and Meta AI. Please provide the name of this university.

Your answer should be the name of the university


Question: In this paper, which example is taken to explain their model's limitation of low model-human similarity?

Your answer should be a string, a phrase which indicates the example and how it works.

Additionally, model performance in our evaluation setup may be affected by the domain gap between models' training data and the stimuli used in our benchmark; for example, TROG uses cartoon depictions of events, which are dissimilar to the more photorealistic training data of CLIP. Thus, ourevaluation results likely represent a lower bound on model-human similarity. Children as young as two years of age are able to learn from and generalise to pictographic depictions of objects [57-59], however, suggesting that generalisation across representations is an early-acquired skill.

Question: Which example is taken to explain their model's limitation of low model-human similarity?


Question: According to the oral paper at ICLR 2024 that adopts Pseudo-Huber losses to replace LPIPS, what Frechet Inception Distance (FID) scores does it achieve on CIFAR-10 and ImageNet 64x64?

Your answer should be a Python list of two float values rounded to 2 decimal places, representing the FID scores achieved on CIFAR-10 and ImageNet 64x64 respectively.


Question: In the paper that proposes SOFO, on which website can I find the author's own flexible implementation of batched JVPs based on OCaml-Torch?

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


Question: In the paper that proposes dSTAR, which method performs best on CIFAR10 under Empire and Little attack respectively?

Your answer should be a Python list of two strings of the name of the method.


Question: In this paper, how many different tasks are illustrated in Figure 2?


Question: What are the key innovations of DiffMatch?

Your answer should be a string, a sentence.

We propose DiffMatch, a novel conditional diffusion-based framework designed to explicitly model both the data and prior terms for dense matching.

Unlike existing discriminative learning-based methods that focus solely on maximizing the likelihood, DiffMatch aims to learn the posterior distribution of dense correspondence.

Question: What are the key innovations of DiffMatch?


Question: A paper derive Contrastive Preference Learning (CPL), an algorithm for learning optimal policies from preferences without learning reward functions. In the figure which shows ablations on CPL's hyperparameters on Drawer Open from State, from which training step, because of an event, the model's success rate increases sharply?

Your answer should be an int giving the number of training step.


Question: In the paper that This paper uniquely demonstrates that all current private adaptation methods for closed LLMs fundamentally leak data, while open LLMs with local training provide superior privacy, performance and cost efficiency, in which dataset does the PromptPATE have the highest Top1 Accuracy in figure 2?

Your answer should be s Python string indicating the name of the dataset.


Question: There is a paper that introduces an interactive formal verification environment that leverages large language models (LLMs) by translating code into Isabelle for theorem proving. It also proposes a large-scale dataset called FVELER, which includes 758 Isabelle theories, 29,125 lemmas, and 200,646 proof steps, extracted from seL4 verification. Could you please tell me how many proof steps are included in the test set of the FVELER dataset?


Question: In ICLR 2024 Poster papers, a paper attempts to solve the problem of how to enhance the arithmetic reasoning capabilities of large language models (LLMs) through zero-sample hint optimization. What is the formula of the Cross-Entropy loss given the demonstration data collected process?

Your answer should be the formula in LaTeX format.

Question: In ICLR 2024 Poster papers, a paper attempts to solve the problem of how to enhance the arithmetic reasoning capabilities of large language models (LLMs) through zero-sample hint optimization. What is the formula of the Cross-Entropy loss given the demonstration data collected process?

\mathcal{L}_{\mathrm{CE}}(\theta ; \mathcal{D}_{\mathrm{dem}}^{\ell}) = -\mathbb{E}_{i \in [N], k \sim [K]}\left[ r^{(i, k)} \log \sigma \left( \Upsilon_{\theta}^{(i, k)} \right) + (1 - r^{(i, k)}) \log \left( 1 - \sigma \left( \Upsilon_{\theta}^{(i, k)} \right) \right) \right]


Question: A recent paper introduces the first million-scale multi-modal, multi-turn, open-domain dialogue dataset, containing over 1.08 million dialogues and 1.53 million associated images collected from real-world social media conversations. In the paper, the authors conduct a detailed statistical comparison between their released dataset and another previous multimodal dialogue dataset. Which dataset has a greater "Average Turns per Dialogue"?

Your answer should be the name of the dataset.


Question: A recent paper introduces TAG (Table-to-Graph generation), a novel end-to-end framework for joint document-level entity and relation extraction. TAG unifies coreference resolution and relation extraction using a coarse-to-fine table filling strategy and dynamically constructs latent graphs that encode semantic, relational, and syntactic dependencies. This work is a collaboration between a certain university and TopGraph.AI. Please provide the name of this university.

Your answer should be the name of the university.


Question: For the BF-CNN architecture, how many times does the number of parameters grow when the image resolution increases from 40x40 to 80x80?

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


Question: Tell me what is the "harmonious loss" in regularization term.

Your answer should be the formula in LaTeX format.

Question: Tell me what is the "harmonious loss" in regularization term.

\sum_{i\in\{o,r,d\}}rac{1}{\sigma_i}\mathcal{L}_i(	heta) + \log(1 + \sigma_i)


Question: In ICLR 2024 Poster papers, a paper attempts to conduct long-range dynamic modeling in an interactive environment. What is the affiliation of the corresponding author?


Question: In ICLR 2024 Poster papers, which paper proposes a framework that integrates DRL with classical planning by automatically inducing task structures and substructures from a few demonstrations?

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

Question: In ICLR 2024 Poster papers, which paper proposes a framework that integrates DRL with classical planning by automatically inducing task structures and substructures from a few demonstrations?

Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures


Question: Can you recommend me a paper published in ICLR 2024 that proposes a novel learning-based method called ElliDock, which predicts an elliptic paraboloid to represent the protein-protein docking interface?

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 learning-based method called ElliDock, which predicts an elliptic paraboloid to represent the protein-protein docking interface?

Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction


Question: Previous NFN often depend on permutation symmetries in neural networks' weights, a paper design corresponding equivariant and invariant layers to incorporate scaling/sign-flipping symmetries. In the experiment, when the weights undergo more extensive scaling and permutation, does their model maintains stable performance?


Question: There is a recent paper introducing SuperCon3D, the first dataset combining 3D crystal structures, including both ordered and disordered materials, with experimentally measured superconducting critical temperatures. Please inform me of the name of the corresponding author of this paper.

Your answer should be a name of a person.


Question: What is the average BLEU score improvement achieved by kNN-TL over strong baselines across four low-resource translation tasks?

Your answer should be a Python float value representing the average BLEU score improvement in points, rounded to 1 decimal place.


Question: In the paper "BigVGAN: A Universal Neural Vocoder with Large-Scale Training," the authors utilized a well-known TTS dataset for training purposes. Could you please clarify how this dataset compares to another prominent dataset, LibriSpeech, in terms of the distribution of audio duration per speaker? Specifically, which dataset exhibits a more dispersed and balanced distribution?


Question: A paper introduce a methodology called Induced Model Matching (IMM) to develop a very accurate (often small) predictive model to a full-featured (often larger) model. In their study, which kind of model is considered as a model probablely lacking in features, personalized or general-public?

Your answer should be chosen between "personalized" and "general-public"


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). Tell me the formula of the projected backward gradient in this paper.

Your answer should be the formula in LaTeX format.

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). Tell me the formula of the projected backward gradient in this paper.

\nabla_{\theta} V(s') - \frac{\nabla_{\theta} V(s)^{\top} \nabla_{	heta} V(s')}{\lVert \nabla_{\theta} V(s) \rVert^2} \nabla_{\theta} V(s)


Question: Faced with the problem that the exact mechanism of how teleportation improves convergence in optimizing non-convex objectives remains elusive, what has this paper done to improve?

In this paper, we provide theoretical guarantees on the convergence rate. In particular, we show that stochastic gradient descent (SGD) with teleportation converges to a basin of stationary points, where every point reachable by teleportation is also stationary. We also provide conditions under which one teleportation guarantees optimality of the entire gradient flow trajectory.

Question: Faced with the problem that the exact mechanism of how teleportation improves convergence in optimizing non-convex objectives remains elusive, what has this paper done to improve?


Question: A recent paper provides the first systematic study of benchmark data repositories in machine learning, introducing the concept of a "benchmark repository" as a distinct entity from general-purpose or domain-specific data repositories. Could you please provide the email address of the first author?


Question: Among the Model-based Reinforcement Learning papers in ICLR 2024, which one proposes the model called "Skipper"? Explain the conception of spatio-abstraction and temporal-abstraction in the paper, respectively.

Question: Explain the conception of spatio-abstraction and temporal-abstraction in the paper, respectively.

Spatio-abstraction restricts decisions to relevant environmental factors, while temporal-abstraction introduces a local state perceptron.


Question: What is the average improvement in pass rate achieved by LEGO-Prover over previous methods on the miniF2F dataset?

Your answer should be a Python float value representing the percentage improvement in pass rate, between 0 and 100, rounded to 2 decimal places.


Question: According to this paper, which model performes the second best over all test domains?

Your answer should be a string, a name of model