34 citations · 62 across the 14 of their papers we have counts for
14 papers
InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem
Shuofei Qiao, Yunxiang Wei, Xuehai Wang +10
The rapid evolution of Large Language Models has catalyzed a surge in scientific idea production, yet this leap has not been accompanied by a matching advance in idea evaluation. T…
Knowledge-Augmented Long-CoT Generation for Complex Biomolecular Reasoning
Tianwen Lyu, Xiang Zhuang, Keyan Ding +5
Understanding complex biomolecular mechanisms requires multi-step reasoning across molecular interactions, signaling cascades, and metabolic pathways. While large language models(L…
Breaking the Modality Barrier: Generative Modeling for Accurate Molecule Retrieval from Mass Spectra
Yiwen Zhang, Keyan Ding, Yihang Wu +4
Retrieving molecular structures from tandem mass spectra is a crucial step in rapid compound identification. Existing retrieval methods, such as traditional mass spectral library m…
SciToolAgent: A Knowledge Graph-Driven Scientific Agent for Multi-Tool Integration
Keyan Ding, Jing Yu, Junjie Huang +3
Scientific research increasingly relies on specialized computational tools, yet effectively utilizing these tools demands substantial domain expertise. While Large Language Models…
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…
MKGL: Mastery of a Three-Word Language
Lingbing Guo, Zhongpu Bo, Zhuo Chen +10
Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs),…
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…
SciSafeEval: A Comprehensive Benchmark for Safety Alignment of Large Language Models in Scientific Tasks
Tianhao Li, Jingyu Lu, Chuangxin Chu +12
Large language models (LLMs) have a transformative impact on a variety of scientific tasks across disciplines including biology, chemistry, medicine, and physics. However, ensuring…
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…
StableMask: Refining Causal Masking in Decoder-only Transformer
Qingyu Yin, Xuzheng He, Xiang Zhuang +4
The decoder-only Transformer architecture with causal masking and relative position encoding (RPE) has become the de facto choice in language modeling. Despite its exceptional perf…
Scientific Large Language Models: A Survey on Biological & Chemical Domains
Qiang Zhang, Keyang Ding, Tianwen Lyv +22
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelli…
Learning Invariant Molecular Representation in Latent Discrete Space
Xiang Zhuang, Qiang Zhang, Keyan Ding +5
Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when dat…
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
Zeyuan Wang, Qiang Zhang, Keyan Ding +4
Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address th…