ChemDFM-R: An Chemical Reasoner LLM Enhanced with Atomized Chemical
  Knowledge
While large language models (LLMs) have achieved impressive progress, their
application in scientific domains such as chemistry remains hindered by shallow
domain understanding and limited reasoning capabilities. In this work, we focus
on the specific field of chemistry and develop a Chemical Reasoner LLM,
ChemDFM-R. We first construct a comprehensive dataset of atomized knowledge
points to enhance the model's understanding of the fundamental principles and
logical structure of chemistry. Then, we propose a mix-sourced distillation
strategy that integrates expert-curated knowledge with general-domain reasoning
skills, followed by domain-specific reinforcement learning to enhance chemical
reasoning. Experiments on diverse chemical benchmarks demonstrate that
ChemDFM-R achieves state-of-the-art performance while providing interpretable,
rationale-driven outputs. Further case studies illustrate how explicit
reasoning chains significantly improve the reliability, transparency, and
practical utility of the model in real-world human-AI collaboration scenarios.
688994c731e1218a089281de
Zihan Zhao
688994c731e1218a089281df
Bo Chen
688994c731e1218a089281e0
Ziping Wan
688994c731e1218a089281e1
Lu Chen
688994c731e1218a089281e2
Xuanze Lin
688994c731e1218a089281e3
Shiyang Yu
688994c731e1218a089281e4
Situo Zhang
688994c731e1218a089281e5
Da Ma
688994c731e1218a089281e6
Zichen Zhu
688994c731e1218a089281e7
Danyang Zhang
688994c731e1218a089281e8
Huayang Wang
688994c731e1218a089281e9
Zhongyang Dai
688994c731e1218a089281ea
Liyang Wen
688994c731e1218a089281eb
Xin Chen
688994c731e1218a089281ec
Kai Yu
ChemDFM-R: An Chemical Reasoner LLM Enhanced with Atomized Chemical
  Knowledge
While large language models (LLMs) have achieved impressive progress, their
application in scientific domains such as chemistry remains hindered by shallow
domain understanding and limited reasoning capabilities. In this work, we focus
on the specific field of chemistry and develop a Chemical Reasoner LLM,
ChemDFM-R. We first construct a comprehensive dataset of atomized knowledge
points to enhance the model's understanding of the fundamental principles and
logical structure of chemistry. Then, we propose a mix-sourced distillation
strategy that integrates expert-curated knowledge with general-domain reasoning
skills, followed by domain-specific reinforcement learning to enhance chemical
reasoning. Experiments on diverse chemical benchmarks demonstrate that
ChemDFM-R achieves state-of-the-art performance while providing interpretable,
rationale-driven outputs. Further case studies illustrate how explicit
reasoning chains significantly improve the reliability, transparency, and
practical utility of the model in real-world human-AI collaboration scenarios.
A Chemical Reasoner LLM, ChemDFM-R, enhances chemical reasoning through a comprehensive dataset, mix-sourced distillation, and domain-specific reinforcement learning, achieving state-of-the-art performance with interpretable outputs.
Chemical Reasoner LLM ChemDFM-R atomized knowledge points mix-sourced distillation domain-specific reinforcement learning chemical benchmarks interpretable rationale-driven outputs reasoning chains