title: MolReFlect: Towards In-Context Fine-grained Alignments between Molecules
    and Texts
summary: Molecule discovery is a pivotal research field, impacting everything from the
  medicines we take to the materials we use. Recently, Large Language Models
  (LLMs) have been widely adopted in molecule understanding and generation, yet
  the alignments between molecules and their corresponding captions remain a
  significant challenge. Previous endeavours often treat the molecule as a
  general SMILES string or molecular graph, neglecting the fine-grained
  alignments between the molecular sub-structures and the descriptive textual
  phrases, which are crucial for accurate and explainable predictions. In this
  case, we introduce MolReFlect, a novel teacher-student framework designed to
  contextually perform the molecule-caption alignments in a fine-grained way. Our
  approach initially leverages a larger teacher LLM to label the detailed
  alignments by directly extracting critical phrases from molecule captions or
  SMILES strings and implying them to corresponding sub-structures or
  characteristics. To refine these alignments, we propose In-Context Selective
  Reflection, which retrieves previous extraction results as context examples for
  teacher LLM to reflect and lets a smaller student LLM select from in-context
  reflection and previous extraction results. Finally, we enhance the learning
  process of the student LLM through Chain-of-Thought In-Context Molecule Tuning,
  integrating the fine-grained alignments and the reasoning processes within the
  Chain-of-Thought format. Our experimental results demonstrate that MolReFlect
  enables LLMs like Mistral-7B to significantly outperform the previous
  baselines, achieving SOTA performance on the ChEBI-20 dataset. This advancement
  not only enhances the generative capabilities of LLMs in the molecule-caption
  translation task, but also contributes to a more explainable framework.
paper_authors._id: 6746d91b1c6c176064e0d558
paper_authors.name: Jiatong Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-27T22:19:51.992Z
paper_authors._id: 6438e55cb2ea24b52ebc45ec
paper_authors.avatarUrl: /avatars/e13c7398f77e7e0bd5eed03102aa5c36.svg
paper_authors.fullname: Jiatong LI
paper_authors.type: user
paper_authors.user: phenixace
paper_authors._id: 6746d91b1c6c176064e0d559
paper_authors.name: Yunqing Liu
paper_authors._id: 6746d91b1c6c176064e0d55a
paper_authors.name: Wei Liu
paper_authors._id: 6746d91b1c6c176064e0d55b
paper_authors.name: Jingdi Le
paper_authors._id: 6746d91b1c6c176064e0d55c
paper_authors.name: Di Zhang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-11-28T07:45:08.818Z
paper_authors._id: 64bce15bafd1e46c5504ad38
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64bce15bafd1e46c5504ad38/bQFX1iFbXEBXcQvUNL811.png
paper_authors.fullname: Di Zhang
paper_authors.type: user
paper_authors.user: di-zhang-fdu
paper_authors._id: 6746d91b1c6c176064e0d55d
paper_authors.name: Wenqi Fan
paper_authors._id: 6746d91b1c6c176064e0d55e
paper_authors.name: Dongzhan Zhou
paper_authors._id: 6746d91b1c6c176064e0d55f
paper_authors.name: Yuqiang Li
paper_authors._id: 6746d91b1c6c176064e0d560
paper_authors.name: Qing Li
paper_title: MolReFlect: Towards In-Context Fine-grained Alignments between Molecules
    and Texts
paper_summary: Molecule discovery is a pivotal research field, impacting everything from the
  medicines we take to the materials we use. Recently, Large Language Models
  (LLMs) have been widely adopted in molecule understanding and generation, yet
  the alignments between molecules and their corresponding captions remain a
  significant challenge. Previous endeavours often treat the molecule as a
  general SMILES string or molecular graph, neglecting the fine-grained
  alignments between the molecular sub-structures and the descriptive textual
  phrases, which are crucial for accurate and explainable predictions. In this
  case, we introduce MolReFlect, a novel teacher-student framework designed to
  contextually perform the molecule-caption alignments in a fine-grained way. Our
  approach initially leverages a larger teacher LLM to label the detailed
  alignments by directly extracting critical phrases from molecule captions or
  SMILES strings and implying them to corresponding sub-structures or
  characteristics. To refine these alignments, we propose In-Context Selective
  Reflection, which retrieves previous extraction results as context examples for
  teacher LLM to reflect and lets a smaller student LLM select from in-context
  reflection and previous extraction results. Finally, we enhance the learning
  process of the student LLM through Chain-of-Thought In-Context Molecule Tuning,
  integrating the fine-grained alignments and the reasoning processes within the
  Chain-of-Thought format. Our experimental results demonstrate that MolReFlect
  enables LLMs like Mistral-7B to significantly outperform the previous
  baselines, achieving SOTA performance on the ChEBI-20 dataset. This advancement
  not only enhances the generative capabilities of LLMs in the molecule-caption
  translation task, but also contributes to a more explainable framework.
paper_ai_keywords: Large Language Models (LLMs) SMILES string molecular graph molecular sub-structures descriptive textual phrases teacher-student framework MolReFlect In-Context Selective Reflection Chain-of-Thought In-Context Molecule Tuning ChEBI-20 dataset Chain-of-Thought format