This paper studies the problem of solving complex chemistry problems with
large language models (LLMs). Despite the extensive general knowledge in LLMs
(such as GPT-4), they struggle with chemistry reasoning that requires faithful
grounded reasoning with diverse chemical knowledge and an integrative
understanding of chemical interactions. We propose InstructChem, a new
structured reasoning approach that substantially boosts the LLMs' chemical
reasoning capabilities. InstructChem explicitly decomposes the reasoning into
three critical phrases, including chemical formulae generation by LLMs that
offers the basis for subsequent grounded reasoning, step-by-step reasoning that
makes multi-step derivations with the identified formulae for a preliminary
answer, and iterative review-and-refinement that steers LLMs to progressively
revise the previous phases for increasing confidence, leading to the final
high-confidence answer. We conduct extensive experiments on four different
chemistry challenges, including quantum chemistry, quantum mechanics, physical
chemistry, and chemistry kinetics. Our approach significantly enhances GPT-4 on
chemistry reasoning, yielding an 8% average absolute improvement and a 30% peak
improvement. We further use the generated reasoning by GPT-4 to fine-tune
smaller LMs (e.g., Vicuna) and observe strong improvement of the smaller LMs.
This validates our approach and enables LLMs to generate high-quality
reasoning.

本文研究利用大型语言模型（LLMs）解决复杂的化学问题的问题，提出了 InstructChem，一种新的结构化推理方法，极大地提升了 LLMs 的化学推理能力。