title: Micro-Act: Mitigate Knowledge Conflict in Question Answering via
    Actionable Self-Reasoning
summary: Retrieval-Augmented Generation (RAG) systems commonly suffer from Knowledge
  Conflicts, where retrieved external knowledge contradicts the inherent,
  parametric knowledge of large language models (LLMs). It adversely affects
  performance on downstream tasks such as question answering (QA). Existing
  approaches often attempt to mitigate conflicts by directly comparing two
  knowledge sources in a side-by-side manner, but this can overwhelm LLMs with
  extraneous or lengthy contexts, ultimately hindering their ability to identify
  and mitigate inconsistencies. To address this issue, we propose Micro-Act a
  framework with a hierarchical action space that automatically perceives context
  complexity and adaptively decomposes each knowledge source into a sequence of
  fine-grained comparisons. These comparisons are represented as actionable
  steps, enabling reasoning beyond the superficial context. Through extensive
  experiments on five benchmark datasets, Micro-Act consistently achieves
  significant increase in QA accuracy over state-of-the-art baselines across all
  5 datasets and 3 conflict types, especially in temporal and semantic types
  where all baselines fail significantly. More importantly, Micro-Act exhibits
  robust performance on non-conflict questions simultaneously, highlighting its
  practical value in real-world RAG applications.
paper_authors._id: 68426dfeb5f4d2d0f8fd098e
paper_authors.name: Nan Huo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:40:24.419Z
paper_authors._id: 60adfff0306d6873ec42d545
paper_authors.avatarUrl: /avatars/4a63f90638dbffebfeeee181a6d0220c.svg
paper_authors.fullname: Nan
paper_authors.type: user
paper_authors.user: NanHUO
paper_authors._id: 68426dfeb5f4d2d0f8fd098f
paper_authors.name: Jinyang Li
paper_authors._id: 68426dfeb5f4d2d0f8fd0990
paper_authors.name: Bowen Qin
paper_authors._id: 68426dfeb5f4d2d0f8fd0991
paper_authors.name: Ge Qu
paper_authors._id: 68426dfeb5f4d2d0f8fd0992
paper_authors.name: Xiaolong Li
paper_authors._id: 68426dfeb5f4d2d0f8fd0993
paper_authors.name: Xiaodong Li
paper_authors._id: 68426dfeb5f4d2d0f8fd0994
paper_authors.name: Chenhao Ma
paper_authors._id: 68426dfeb5f4d2d0f8fd0995
paper_authors.name: Reynold Cheng
paper_title: Micro-Act: Mitigate Knowledge Conflict in Question Answering via
    Actionable Self-Reasoning
paper_summary: Retrieval-Augmented Generation (RAG) systems commonly suffer from Knowledge
  Conflicts, where retrieved external knowledge contradicts the inherent,
  parametric knowledge of large language models (LLMs). It adversely affects
  performance on downstream tasks such as question answering (QA). Existing
  approaches often attempt to mitigate conflicts by directly comparing two
  knowledge sources in a side-by-side manner, but this can overwhelm LLMs with
  extraneous or lengthy contexts, ultimately hindering their ability to identify
  and mitigate inconsistencies. To address this issue, we propose Micro-Act a
  framework with a hierarchical action space that automatically perceives context
  complexity and adaptively decomposes each knowledge source into a sequence of
  fine-grained comparisons. These comparisons are represented as actionable
  steps, enabling reasoning beyond the superficial context. Through extensive
  experiments on five benchmark datasets, Micro-Act consistently achieves
  significant increase in QA accuracy over state-of-the-art baselines across all
  5 datasets and 3 conflict types, especially in temporal and semantic types
  where all baselines fail significantly. More importantly, Micro-Act exhibits
  robust performance on non-conflict questions simultaneously, highlighting its
  practical value in real-world RAG applications.
paper_ai_summary: A framework called Micro-Act addresses Knowledge Conflicts in Retrieval-Augmented Generation by adaptively decomposing knowledge sources, leading to improved QA accuracy compared to existing methods.
paper_ai_keywords: Retrieval-Augmented Generation Knowledge Conflicts large language models parametric knowledge question answering hierarchical action space context complexity fine-grained comparisons actionable steps benchmark datasets QA accuracy conflict types temporal conflicts semantic conflicts non-conflict questions