ChemLLM: A Chemical Large Language Model
Di Zhang Wei Liu Qian Tan Jingdan Chen Hang Yan Yuliang Yan Jiatong Li Weiran Huang Xiangyu Yue Dongzhan Zhou Shufei Zhang Mao Su Hansen Zhong Yuqiang Li Wanli Ouyang
Large language models (LLMs) have made impressive progress in chemistry
applications, including molecular property prediction, molecular generation,
experimental protocol design, etc. However, the community lacks a
dialogue-based model specifically designed for chemistry. The challenge arises
from the fact that most chemical data and scientific knowledge are primarily
stored in structured databases, and the direct use of these structured data
compromises the model's ability to maintain coherent dialogue. To tackle this
issue, we develop a novel template-based instruction construction method that
transforms structured knowledge into plain dialogue, making it suitable for
language model training. By leveraging this approach, we develop ChemLLM, the
first large language model dedicated to chemistry, capable of performing
various tasks across chemical disciplines with smooth dialogue interaction.
ChemLLM beats GPT-3.5 on all three principal tasks in chemistry, i.e., name
conversion, molecular caption, and reaction prediction, and surpasses GPT-4 on
two of them. Remarkably, ChemLLM also shows exceptional adaptability to related
mathematical and physical tasks despite being trained mainly on
chemical-centric corpora. Furthermore, ChemLLM demonstrates proficiency in
specialized NLP tasks within chemistry, such as literature translation and
cheminformatic programming. ChemLLM opens up a new avenue for exploration
within chemical studies, while our method of integrating structured chemical
knowledge into dialogue systems sets a new frontier for developing LLMs across
various scientific fields. Codes, Datasets, and Model weights are publicly
accessible at hf.co/AI4Chem/ChemLLM-7B-Chat.