Xiaoyin Chen

Ehsan Kamalloo

2025-05-20

ArXiv (prépublication)

Search-Based Correction of Reasoning Chains for Language Models

Minsu Kim

Jean-Pierre R. Falet

Oliver E. Richardson

Moksh J. Jain

Sungjin Ahn

Sungsoo Ahn

2025-05-17

ArXiv (prépublication)

Aligning Protein Conformation Ensemble Generation with Physical Feedback

Jiarui Lu

Stephen Zhewen Lu

Aurelie Lozano

Vijil Chenthamarakshan

Payel Das

Jian Tang

Protein dynamics play a crucial role in protein biological functions and properties, and their traditional study typically relies on time-co… (voir plus)nsuming molecular dynamics (MD) simulations conducted in silico. Recent advances in generative modeling, particularly denoising diffusion models, have enabled efficient accurate protein structure prediction and conformation sampling by learning distributions over crystallographic structures. However, effectively integrating physical supervision into these data-driven approaches remains challenging, as standard energy-based objectives often lead to intractable optimization. In this paper, we introduce Energy-based Alignment (EBA), a method that aligns generative models with feedback from physical models, efficiently calibrating them to appropriately balance conformational states based on their energy differences. Experimental results on the MD ensemble benchmark demonstrate that EBA achieves state-of-the-art performance in generating high-quality protein ensembles. By improving the physical plausibility of generated structures, our approach enhances model predictions and holds promise for applications in structural biology and drug discovery.

2025-05-01

ICML.cc/2025/Conference (poster)

Search-Based Correction of Reasoning Chains for Language Models

Minsu Kim

Jean-Pierre R. Falet

Oliver E. Richardson

Moksh J. Jain

Sungjin Ahn

Sungsoo Ahn

2025-05-01

arXiv (publié)

Self-Evolving Curriculum for LLM Reasoning

Minsu Kim

Alexandre Piché

Nicolas Gontier

Ehsan Kamalloo

2025-05-01

arXiv (publié)

HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models

Seanie Lee

Haebin Seong

Dong Bok Lee

Minki Kang

Dominik Wagner

Juho Lee

Sung Ju Hwang

Safety guard models that detect malicious queries aimed at large language models (LLMs) are essential for ensuring the secure and responsibl… (voir plus)e deployment of LLMs in real-world applications. However, deploying existing safety guard models with billions of parameters alongside LLMs on mobile devices is impractical due to substantial memory requirements and latency. To reduce this cost, we distill a large teacher safety guard model into a smaller one using a labeled dataset of instruction-response pairs with binary harmfulness labels. Due to the limited diversity of harmful instructions in the existing labeled dataset, naively distilled models tend to underperform compared to larger models. To bridge the gap between small and large models, we propose HarmAug, a simple yet effective data augmentation method that involves jailbreaking an LLM and prompting it to generate harmful instructions. Given a prompt such as,"Make a single harmful instruction prompt that would elicit offensive content", we add an affirmative prefix (e.g.,"I have an idea for a prompt:") to the LLM's response. This encourages the LLM to continue generating the rest of the response, leading to sampling harmful instructions. Another LLM generates a response to the harmful instruction, and the teacher model labels the instruction-response pair. We empirically show that our HarmAug outperforms other relevant baselines. Moreover, a 435-million-parameter safety guard model trained with HarmAug achieves an F1 score comparable to larger models with over 7 billion parameters, and even outperforms them in AUPRC, while operating at less than 25% of their computational cost.

2025-01-22

ICLR.cc/2025/Conference (poster)

Structure Language Models for Protein Conformation Generation

Jiarui Lu

Stephen Zhewen Lu

Chence Shi

Hongyu Guo

Jian Tang

2025-01-22

ICLR.cc/2025/Conference (poster)

Efficient Causal Graph Discovery Using Large Language Models

Yash More

2024-03-05

ICLR.cc/2024/Workshop/AGI (poster)

Efficient Causal Graph Discovery Using Large Language Models

Yash More

We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise quer… (voir plus)y approach, this requires a quadratic number of queries which quickly becomes impractical for larger causal graphs. In contrast, the proposed framework uses a breadth-first search (BFS) approach which allows it to use only a linear number of queries. We also show that the proposed method can easily incorporate observational data when available, to improve performance. In addition to being more time and data-efficient, the proposed framework achieves state-of-the-art results on real-world causal graphs of varying sizes. The results demonstrate the effectiveness and efficiency of the proposed method in discovering causal relationships, showcasing its potential for broad applicability in causal graph discovery tasks across different domains.

2024-02-02

ArXiv (prépublication)