25 citations · 36 across the 14 of their papers we have counts for
15 papers
Can LLMs Value the Right Evidence? Evidence-Value Misalignment in Dynamic Medical Diagnosis
Kehua Feng, Yunsheng Lu, Yitong Qiao +5
A correct diagnosis reached from insufficient or misleading evidence can pose a clinical hazard, yet outcome-based accuracy may reward such lucky guesses. We call this mismatch bet…
ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding
Mingyang Rao, Kehua Feng, Zhihui Zhu +4
While Large Language Models (LLMs) have revolutionized scientific text processing, they exhibit a significant capability gap when interpreting chemical reaction diagrams. We identi…
Embodied Science: Closing the Discovery Loop with Agentic Embodied AI
Xiang Zhuang, Chenyi Zhou, Kehua Feng +10
Artificial intelligence has demonstrated remarkable capability in predicting scientific properties, yet scientific discovery remains an inherently physical, long-horizon pursuit go…
Evaluating Reward Model Generalization via Pairwise Maximum Discrepancy Competitions
Shunyang Luo, Peibei Cao, Zhihui Zhu +3
Reward models (RMs) are central to aligning large language models, yet their practical effectiveness hinges on generalization to unseen prompts and shifting distributions. Most exi…
ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning
Yuqi Tang, Jing Yu, Zichang Su +7
Clinical diagnosis begins with doctor-patient interaction, during which physicians iteratively gather information, determine examination and refine differential diagnosis through p…
CoT-Evo: Evolutionary Distillation of Chain-of-Thought for Scientific Reasoning
Kehua Feng, Keyan Ding, Zhihui Zhu +3
While chain-of-thought (CoT) distillation from advanced large language models (LLMs) has proven effective in general reasoning tasks, it struggles in scientific domains where even…
Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple LLM Judges
Yuqi Tang, Kehua Feng, Yunfeng Wang +6
Evaluating the conversational abilities of large language models (LLMs) remains a challenging task. Current mainstream approaches primarily rely on the "LLM-as-a-judge" paradigm, w…
Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization
Yuhao Wang, Keyan Ding, Kehua Feng +5
Protein language models have emerged as powerful tools for sequence generation, offering substantial advantages in functional optimization and denovo design. However, these models…
Boosting LLM's Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning
Xiang Zhuang, Bin Wu, Jiyu Cui +6
Molecular structure elucidation involves deducing a molecule's structure from various types of spectral data, which is crucial in chemical experimental analysis. While large langua…
SciCUEval: A Comprehensive Dataset for Evaluating Scientific Context Understanding in Large Language Models
Jing Yu, Yuqi Tang, Kehua Feng +8
Large Language Models (LLMs) have shown impressive capabilities in contextual understanding and reasoning. However, evaluating their performance across diverse scientific domains r…
SAFER: Advancing Safety Alignment via Efficient Ex-Ante Reasoning
Kehua Feng, Keyan Ding, Yuhao Wang +5
Recent advancements in large language models (LLMs) have accelerated progress toward artificial general intelligence, yet their potential to generate harmful content poses critical…
Advancing biomolecular understanding and design following human instructions
Xiang Zhuang, Keyan Ding, Tianwen Lyu +9
Understanding and designing biomolecules, such as proteins and small molecules, is central to advancing drug discovery, synthetic biology and enzyme engineering. Recent breakthroug…
SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models
Kehua Feng, Xinyi Shen, Weijie Wang +4
Large language models (LLMs) are playing an increasingly important role in scientific research, yet there remains a lack of comprehensive benchmarks to evaluate the breadth and dep…
Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition
Kehua Feng, Keyan Ding, Hongzhi Tan +8
Reliable evaluation of large language models (LLMs) is impeded by two key challenges: objective metrics often fail to reflect human perception of natural language, and exhaustive h…
FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios
I-Chun Chern, Steffi Chern, Shiqi Chen +6
The emergence of generative pre-trained models has facilitated the synthesis of high-quality text, but it has also posed challenges in identifying factual errors in the generated t…