title: ChatCell: Facilitating Single-Cell Analysis with Natural Language
summary: As Large Language Models (LLMs) rapidly evolve, their influence in science is
  becoming increasingly prominent. The emerging capabilities of LLMs in task
  generalization and free-form dialogue can significantly advance fields like
  chemistry and biology. However, the field of single-cell biology, which forms
  the foundational building blocks of living organisms, still faces several
  challenges. High knowledge barriers and limited scalability in current methods
  restrict the full exploitation of LLMs in mastering single-cell data, impeding
  direct accessibility and rapid iteration. To this end, we introduce ChatCell,
  which signifies a paradigm shift by facilitating single-cell analysis with
  natural language. Leveraging vocabulary adaptation and unified sequence
  generation, ChatCell has acquired profound expertise in single-cell biology and
  the capability to accommodate a diverse range of analysis tasks. Extensive
  experiments further demonstrate ChatCell's robust performance and potential to
  deepen single-cell insights, paving the way for more accessible and intuitive
  exploration in this pivotal field. Our project homepage is available at
  https://zjunlp.github.io/project/ChatCell.
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paper_authors.name: Yin Fang
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paper_authors.name: Ningyu Zhang
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paper_authors.fullname: Ningyu Zhang
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paper_authors.name: Xinle Deng
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paper_authors.fullname: Xinle Deng
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paper_authors.user: Linear-Matrix-Probability
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paper_authors.name: Penghui Yang
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paper_authors.fullname: Penghui Yang
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paper_authors.user: phyang
paper_authors._id: 65cc44f594510701e89938b4
paper_authors.name: Zhuo Chen
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paper_authors.fullname: Zhuo Chen
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paper_authors.user: HackCz
paper_authors._id: 65cc44f594510701e89938b5
paper_authors.name: Xiangru Tang
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paper_authors._id: 63357c608adfa81faf2ac180
paper_authors.avatarUrl: /avatars/ae0314c644f882251baf59b9134fd36f.svg
paper_authors.fullname: Xiangru Tang
paper_authors.type: user
paper_authors.user: RTT1
paper_authors._id: 65cc44f594510701e89938b6
paper_authors.name: Mark Gerstein
paper_authors._id: 65cc44f594510701e89938b7
paper_authors.name: Xiaohui Fan
paper_authors._id: 65cc44f594510701e89938b8
paper_authors.name: Huajun Chen
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paper_authors.fullname: Huajun Chen
paper_authors.type: user
paper_authors.user: huajunsir
paper_title: ChatCell: Facilitating Single-Cell Analysis with Natural Language
paper_summary: As Large Language Models (LLMs) rapidly evolve, their influence in science is
  becoming increasingly prominent. The emerging capabilities of LLMs in task
  generalization and free-form dialogue can significantly advance fields like
  chemistry and biology. However, the field of single-cell biology, which forms
  the foundational building blocks of living organisms, still faces several
  challenges. High knowledge barriers and limited scalability in current methods
  restrict the full exploitation of LLMs in mastering single-cell data, impeding
  direct accessibility and rapid iteration. To this end, we introduce ChatCell,
  which signifies a paradigm shift by facilitating single-cell analysis with
  natural language. Leveraging vocabulary adaptation and unified sequence
  generation, ChatCell has acquired profound expertise in single-cell biology and
  the capability to accommodate a diverse range of analysis tasks. Extensive
  experiments further demonstrate ChatCell's robust performance and potential to
  deepen single-cell insights, paving the way for more accessible and intuitive
  exploration in this pivotal field. Our project homepage is available at
  https://zjunlp.github.io/project/ChatCell.
paper_ai_keywords: Large Language Models (LLMs) task generalization free-form dialogue single-cell biology high knowledge barriers limited scalability single-cell data ChatCell vocabulary adaptation unified sequence generation single-cell analysis natural language robust performance single-cell insights