GenAI4ELab/papercli-papers huggingface dataset:
A Generalist Hanabi Agent
Authors: Arjun V Sudhakar; Hadi Nekoei; Mathieu Reymond; Miao Liu; Janarthanan Rajendran; Sarath Chandar
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Traditional multi-agent reinforcement learning (MARL) systems can develop cooperative strategies through repeated interactions. However, these systems are unable to perform well on any other setting than the one they have been trained on, and struggle to successfully cooperate with unfamiliar collaborators. This is particularly visible in the Hanabi benchmark, a popular 2-to-5 player cooperative card-game which requires complex reasoning and precise assistance to other agents. Current MARL agents for Hanabi can only learn one specific game-setting (e.g., 2-player games), and play with the same algorithmic agents. This is in stark contrast to humans, who can quickly adjust their strategies to work with unfamiliar partners or situations. In this paper, we introduce Recurrent Replay Relevance Distributed DQN (R3D2), a generalist agent for Hanabi, designed to overcome these limitations. We reformulate the task using text, as language has been shown to improve transfer. We then propose a distributed MARL algorithm that copes with the resulting dynamic observation- and action-space. In doing so, our agent is the first that can play all game settings concurrently, and extend strategies learned from one setting to other ones. As a consequence, our agent also demonstrates the ability to collaborate with different algorithmic agents ---agents that are themselves unable to do so.
Exact Certification of (Graph) Neural Networks Against Label Poisoning
Authors: Mahalakshmi Sabanayagam; Lukas Gosch; Stephan Günnemann; Debarghya Ghoshdastidar
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Machine learning models are highly vulnerable to label flipping, i.e., the adversarial modification (poisoning) of training labels to compromise performance. Thus, deriving robustness certificates is important to guarantee that test predictions remain unaffected and to understand worst-case robustness behavior. However, for Graph Neural Networks (GNNs), the problem of certifying label flipping has so far been unsolved. We change this by introducing an exact certification method, deriving both sample-wise and collective certificates. Our method leverages the Neural Tangent Kernel (NTK) to capture the training dynamics of wide networks enabling us to reformulate the bilevel optimization problem representing label flipping into a Mixed-Integer Linear Program (MILP). We apply our method to certify a broad range of GNN architectures in node classification tasks. Thereby, concerning the worst-case robustness to label flipping: $(i)$ we establish hierarchies of GNNs on different benchmark graphs; $(ii)$ quantify the effect of architectural choices such as activations, depth and skip-connections; and surprisingly, $(iii)$ uncover a novel phenomenon of the robustness plateauing for intermediate perturbation budgets across all investigated datasets and architectures. While we focus on GNNs, our certificates are applicable to sufficiently wide NNs in general through their NTK. Thus, our work presents the first exact certificate to a poisoning attack ever derived for neural networks, which could be of independent interest. The code is available at https://github.com/saper0/qpcert.
GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation
Authors: Tao Feng; Yihang Sun; Jiaxuan You
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The powerful capabilities of Large Language Models (LLMs) have led to their growing use in evaluating human-generated content, particularly in evaluating research ideas within academic settings. Existing solutions primarily rely on prompt-based LLM methods or fine-tuned lightweight language models for idea evaluation. However, these methods are often unstable and struggle to comprehend the complex semantic information embedded in the ideas, impeding their ability to perform high-quality evaluations. To address the above challenges, we propose $\texttt{GraphEval}$, a lightweight graph-based LLM framework for idea evaluation. Our insight is that a complex idea can be broken down into comprehensible viewpoint nodes using prompts from small LLMs. These viewpoint nodes can then be linked together through edges created from LLM-based relation extraction and/or BERT similarity scores. The created viewpoint-graph can be used to conveniently propagate scores across view-nodes to improve the robustness of the idea evaluations. In particular, we propose two lightweight graph-based methods for idea evaluation: (1) GraphEval-LP: a training-free label propagation algorithm that propagates evaluation scores from known view-nodes to unknown nodes; (2) GraphEval-GNN: a Graph Neural Networks (GNN) that is trained to predict the evaluation scores given the observed graph with minimal computation resources. Moreover, to overcome LLM's limitation in objectively assessing the novelty of ideas, we further propose a novelty detection model to GraphEval-GNN to enhance its capability in judging idea novelty. Experiments on two datasets show $\texttt{GraphEval}$ improves F1 scores by at least 14% with low computation and API costs. Additionally, $\texttt{GraphEval}$ can effectively detect plagiarized ideas.
Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questions
Authors: Michael JQ Zhang; W. Bradley Knox; Eunsol Choi
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large language models (LLMs) must often respond to highly ambiguous user requests. In such cases, the LLM's best response may be to ask a clarifying question to elicit more information. Existing LLMs often respond by presupposing a single interpretation of such ambiguous requests, frustrating users who intended a different interpretation. We speculate this is caused by current preference data labeling practice, where LLM responses are evaluated only on their prior contexts. To address this, we assign preference labels by simulating their expected outcomes in future turns. This allows LLMs to learn to ask clarifying questions when it can generate responses that are tailored to each user interpretation in future turns. On open-domain QA datasets with multiple annotations, we evaluate systems based on their ability to ask clarifying questions to recover each user's interpretation and expected answer. We compare systems trained using our proposed preference labeling methods against standard methods, which assign preferences based on only prior context. Our method achieves a 5% improvement in F1 measured against the answer set from different interpretations of each query, showing the value of modeling future conversation turns. We further demonstrate that our method can be used to train models to judiciously determine when to ask clarifying questions, directly answering the question when clarification is unnecessary. In our experiments, we find that our method achives a 3% improvement in accuracy of such judgments over existing methods.
Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs
Authors: Sungmin Cha; Sungjun Cho; Dasol Hwang; Moontae Lee
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large Language Models (LLMs) have demonstrated strong reasoning and memorization capabilities via pretraining on massive textual corpora. However, this poses risk of privacy and copyright violations, highlighting the need for efficient machine unlearning methods that remove sensitive data without retraining from scratch. While Gradient Ascent (GA) is commonly used to unlearn by reducing the likelihood of generating unwanted content, it leads to unstable optimization and catastrophic forgetting of retrained knowledge. We find that combining GA with low-rank adaptation results in poor trade-offs between computational cost and generative performance. To address these challenges, we propose Low-rank Knowledge Unlearning (LoKU), a novel framework that enables robust and efficient unlearning for LLMs. First, we introduce Inverted Hinge Loss, which suppresses unwanted tokens while maintaining fluency by boosting the probability of the next most likely token. Second, we develop a data-adaptive initialization for LoRA adapters via low-rank approximation weighted with relative Fisher information, thereby focusing updates on parameters critical for removing targeted knowledge. Experiments on the Training Data Extraction Challenge dataset using GPT-Neo models as well as on the TOFU benchmark with Phi-1.5B and Llama2-7B models demonstrate that our approach effectively removes sensitive information while maintaining reasoning and generative capabilities with minimal impact. Our implementation can be found in https://github.com/csm9493/efficient-llm-unlearning.
What Makes Large Language Models Reason in (Multi-Turn) Code Generation?
Authors: Kunhao Zheng; Juliette Decugis; Jonas Gehring; Taco Cohen; benjamin negrevergne; Gabriel Synnaeve
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Prompting techniques such as chain-of-thought have established themselves as a popular vehicle for improving the outputs of large language models (LLMs). For code generation, however, their exact mechanics and efficacy are under-explored using unified metrics and benchmarks. We thus investigate the effects of a wide range of prompting strategies with a focus on automatic re-prompting over multiple turns and computational requirements. After systematically decomposing reasoning, instruction, and execution feedback prompts, we conduct an extensive grid search on the competitive programming benchmarks CodeContests and TACO for multiple LLM families and sizes (Llama 3.0 and 3.1, 8B, 70B, 405B, and GPT-4o). Our study reveals strategies that consistently improve performance across all models with small and large sampling budgets. We then show how finetuning with such an optimal configuration allows models to internalize the induced reasoning process and obtain improvements in performance and scalability for multi-turn code generation.
RMB: Comprehensively benchmarking reward models in LLM alignment
Authors: Enyu Zhou; Guodong Zheng; Binghai Wang; Zhiheng Xi; Shihan Dou; Rong Bao; Wei Shen; Limao Xiong; Jessica Fan; Yurong Mou; Rui Zheng; Tao Gui; Qi Zhang; Xuanjing Huang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Reward models (RMs) guide the alignment of large language models (LLMs), steering them toward behaviors preferred by humans. Evaluating RMs is the key to better aligning LLMs. However, the current evaluation of RMs may not directly correspond to their alignment performance due to the limited distribution of evaluation data and evaluation methods that are not closely related to alignment objectives. To address these limitations, we propose RMB, a comprehensive RM benchmark that covers over 49 real-world scenarios and includes both pairwise and Best-of-N (BoN) evaluations to better reflect the effectiveness of RMs in guiding alignment optimization.
We demonstrate a positive correlation between our benchmark and the downstream alignment task performance. Based on our benchmark, we conduct extensive analysis on the state-of-the-art RMs, revealing their generalization defects that were not discovered by previous benchmarks, and highlighting the potential of generative RMs.  Furthermore, we delve into open questions in reward models, specifically examining the effectiveness of majority voting for the evaluation of reward models and analyzing the impact factors of generative RMs, including the influence of evaluation criteria and instructing methods. We will release our evaluation code and datasets upon publication.
FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine
Authors: Shannon How Shi Qi; Jagmohan Chauhan; Geoff V. Merrett; Jonathon Hare
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: One-Shot Federated Learning (OFL) is a promising approach that reduce communication to a single round, minimizing latency and resource consumption. However, existing OFL methods often rely on Knowledge Distillation, which introduce server-side training, increasing latency. While neuron matching and model fusion techniques bypass server-side training, they struggle with alignment when heterogeneous data is present. To address these challenges, we proposed One-Shot Federated Learning with Tsetlin Machine (FedTMOS), a novel data-free OFL framework built upon the low-complexity and class-adaptive properties of the Tsetlin Machine. FedTMOS first clusters then reassigns class-specific weights to form models using an inter-class maximization approach, efficiently generating balanced server models without requiring additional training. Our extensive experiments demonstrate that FedTMOS significantly outperforms its ensemble counterpart by an average of $6.16$%, and the leading state-of-the-art OFL baselines by $7.22$% across various OFL settings. Moreover, FedTMOS achieves at least a $2.3\times$ reduction in upload communication costs and a $75\times$ reduction in server latency compared to methods requiring server-side training. These results establish FedTMOS as a highly efficient and practical solution for OFL scenarios.
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency
Authors: Kaiyue Wen; Huaqing Zhang; Hongzhou Lin; Jingzhao Zhang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Chain-of-thought (CoT)  significantly enhances the reasoning performance of large language models (LLM). While current theoretical studies often attribute this improvement to increased expressiveness and computational capacity, we argue that expressiveness is not the primary limitation in the LLM regime, as current large models will fail on simple tasks. Using a parity-learning setup, we demonstrate that CoT can substantially improve sample efficiency even when the representation power is sufficient. Specifically, with CoT, a transformer can learn the function within polynomial samples, whereas without CoT, the required sample size is exponential. Additionally, we show that CoT simplifies the learning process by introducing sparse sequential dependencies among input tokens, and leads to a sparse and interpretable attention. We validate our theoretical analysis with both synthetic and real-world experiments, confirming that sparsity in attention layers is a key factor of the improvement induced by CoT.
CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery
Authors: Xiaoshuai Song; Muxi Diao; Guanting Dong; Zhengyang Wang; Yujia Fu; Runqi Qiao; Zhexu Wang; Dayuan Fu; Huangxuan Wu; Bin Liang; Weihao Zeng; Yejie Wang; Zhuoma GongQue; Jianing Yu; Qiuna Tan; Weiran Xu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large language models (LLMs) have demonstrated significant potential in advancing various fields of research and society. However, the current community of LLMs overly focuses on benchmarks for analyzing specific foundational skills (e.g. mathematics and code generation), neglecting an all-round evaluation of the computer science field. To bridge this gap, we introduce CS-Bench, the first multilingual (English, Chinese, French, German) benchmark dedicated to evaluating the performance of LLMs in computer science. CS-Bench comprises approximately 10K meticulously curated test samples, covering 26 subfields across 4 key areas of computer science, encompassing various task forms and divisions of knowledge and reasoning. Utilizing CS-Bench, we conduct a comprehensive evaluation of over 30 mainstream LLMs, revealing the relationship between CS performance and model scales. We also quantitatively analyze the reasons for failures in existing LLMs and highlight directions for improvements, including knowledge supplementation and CS-specific reasoning. Further cross-capability experiments show a high correlation between LLMs' capabilities in computer science and their abilities in mathematics and coding. Moreover, expert LLMs specialized in mathematics and coding also demonstrate strong performances in several CS subfields. Looking ahead, we envision CS-Bench serving as a cornerstone for LLM applications in the CS field and paving new avenues in assessing LLMs' diverse reasoning capabilities. Our project homepage is available at https://csbench.github.io/.
A deep inverse-mapping model for a flapping robotic wing
Authors: Hadar Sharvit; Raz Karl; Tsevi Beatus
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: In systems control, the dynamics of a system are governed by modulating its inputs to achieve a desired outcome. For example, to control the thrust of a quad-copter propeller the controller modulates its rotation rate, relying on a straightforward mapping between the input rotation rate and the resulting thrust. This mapping can be inverted to determine the rotation rate needed to generate a desired thrust. However, in complex systems, such as flapping-wing robots where intricate fluid motions are involved, mapping inputs (wing kinematics) to outcomes (aerodynamic forces) is nontrivial and inverting this mapping for real-time control is computationally impractical. Here, we report a machine-learning solution for the inverse mapping of a flapping-wing system based on data from an experimental system we have developed. Our model learns the input wing motion required to generate a desired aerodynamic force outcome. We used a sequence-to-sequence model tailored for time-series data and augmented it with a novel adaptive-spectrum layer that implements representation learning in the frequency domain. To train our model, we developed a flapping wing system that simultaneously measures the wing's aerodynamic force and its 3D motion using high-speed cameras. We demonstrate the performance of our system on an additional open-source dataset of a flapping wing in a different flow regime. Results show superior performance compared with more complex state-of-the-art transformer-based models, with 11\% improvement on the test datasets median loss. Moreover, our model shows superior inference time, making it practical for onboard robotic control. Our open-source data and framework may improve modeling and real-time control of systems governed by complex dynamics, from biomimetic robots to biomedical devices.
Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal Perspective
Authors: Xiangru Zhu; Penglei Sun; Yaoxian Song; Yanghua Xiao; Zhixu Li; Chengyu Wang; Jun Huang; Bei Yang; Xiaoxiao Xu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Accurate interpretation and visualization of human instructions are crucial for text-to-image (T2I) synthesis. 
However, current models struggle to capture semantic variations from word order changes, and existing evaluations, relying on indirect metrics like text-image similarity, fail to reliably assess these challenges. This often obscures poor performance on complex or uncommon linguistic patterns by the focus on frequent word combinations.
To address these deficiencies, we propose a novel metric called SemVarEffect and a benchmark named SemVarBench, designed to evaluate the causality between semantic variations in inputs and outputs in T2I synthesis. 
Semantic variations are achieved through two types of linguistic permutations, while avoiding easily predictable literal variations.
Experiments reveal that the CogView-3-Plus and Ideogram 2 performed the best, achieving a score of 0.2/1. Semantic variations in object relations are less understood than attributes, scoring 0.07/1 compared to 0.17-0.19/1. 
We found that cross-modal alignment in UNet or Transformers plays a crucial role in handling semantic variations, a factor previously overlooked by a focus on textual encoders. 
Our work establishes an effective evaluation framework that advances the T2I synthesis community's exploration of human instruction understanding. Our benchmark and code are available at https://github.com/zhuxiangru/SemVarBench.
GraphRouter: A Graph-based Router for LLM Selections
Authors: Tao Feng; Yanzhen Shen; Jiaxuan You
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The rapidly growing number and variety of Large Language Models (LLMs)
present significant challenges in efficiently selecting the appropriate LLM for
a given query, especially considering the trade-offs between performance and
computational cost. Current LLM selection methods often struggle to generalize
across new LLMs and different tasks because of their limited ability to leverage
contextual interactions among tasks, queries, and LLMs, as well as their depen-
dence on a transductive learning framework. To address these shortcomings, we
introduce a novel inductive graph framework, named as GraphRouter, which
fully utilizes the contextual information among tasks, queries, and LLMs to en-
hance the LLM selection process. GraphRouter constructs a heterogeneous
graph comprising task, query, and LLM nodes, with interactions represented as
edges, which efficiently captures the contextual information between the query’s
requirements and the LLM’s capabilities. Through an innovative edge prediction
mechanism, GraphRouter is able to predict attributes (the effect and cost of
LLM response) of potential edges, allowing for optimized recommendations that
adapt to both existing and newly introduced LLMs without requiring retraining.
Comprehensive experiments across three distinct effect-cost weight scenarios have
shown that GraphRouter substantially surpasses existing routers, delivering a
minimum performance improvement of 12.3%. In addition, it achieves enhanced
generalization across new LLMs settings and supports diverse tasks with at least a
9.5% boost in effect and a significant reduction in computational demands. This
work endeavors to apply a graph-based approach for the contextual and adaptive
selection of LLMs, offering insights for real-world applications.
CONDA: Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts
Authors: Jihye Choi; Jayaram Raghuram; Yixuan Li; Somesh Jha
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Advancements in foundation models (FMs) have led to a paradigm shift in machine
learning. The rich, expressive feature representations from these pre-trained, large-
scale FMs are leveraged for multiple downstream tasks, usually via lightweight
fine-tuning of a shallow fully-connected network following the representation.
However, the non-interpretable, black-box nature of this prediction pipeline can be
a challenge, especially in critical domains, such as healthcare, finance, and security.
In this paper, we explore the potential of Concept Bottleneck Models (CBMs)
for transforming complex, non-interpretable foundation models into interpretable
decision-making pipelines using high-level concept vectors. Specifically, we focus
on the test-time deployment of such an interpretable CBM pipeline “in the wild”,
where the distribution of inputs often shifts from the original training distribution.
We first identify the potential failure modes of such pipelines under different types
of distribution shifts. Then we propose an adaptive concept bottleneck framework
to address these failure modes, that dynamically adapts the concept-vector bank
and the prediction layer based solely on unlabeled data from the target domain,
without access to the source dataset. Empirical evaluations with various real-world
distribution shifts show our framework produces concept-based interpretations
better aligned with the test data and boosts post-deployment accuracy by up to
28%, aligning CBM performance with that of non-interpretable classification.