title: ChemLLM: A Chemical Large Language Model
summary: 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.
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paper_authors.name: Di Zhang
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paper_authors.fullname: 笨笨狗
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paper_authors.name: Jingdan Chen
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paper_authors.fullname: Jingdan Chen
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paper_authors.user: ChlorineChem
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paper_authors.name: Hang Yan
paper_authors._id: 65cad646ef77197acfba69f3
paper_authors.name: Yuliang Yan
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paper_authors.fullname: Yuliang Yan
paper_authors.type: user
paper_authors.user: Goal-aweiluo
paper_authors._id: 65cad646ef77197acfba69f4
paper_authors.name: Jiatong Li
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/e13c7398f77e7e0bd5eed03102aa5c36.svg
paper_authors.fullname: Jiatong LI
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paper_authors.user: phenixace
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paper_authors.name: Weiran Huang
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paper_authors.fullname: Weiran Huang
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paper_authors.name: Xiangyu Yue
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paper_authors.name: Dongzhan Zhou
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paper_authors.name: Shufei Zhang
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paper_authors.name: Mao Su
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paper_authors.name: Hansen Zhong
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paper_authors.name: Yuqiang Li
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paper_authors.fullname: Yuqiang Li
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paper_authors._id: 65cad646ef77197acfba69fc
paper_authors.name: Wanli Ouyang
paper_title: ChemLLM: A Chemical Large Language Model
paper_summary: 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.
paper_ai_summary: A dialogue-based large language model for chemistry, ChemLLM, demonstrates superior performance over existing models across various chemical tasks and shows adaptability to mathematical and physical tasks by transforming structured chemical knowledge into conversational format.
paper_ai_keywords: template-based instruction construction dialogue-based model ChemLLM name conversion molecular caption reaction prediction literature translation cheminformatic programming