title: REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large
    Language Models
summary: Hallucinations in large language model (LLM) outputs severely limit their
  reliability in knowledge-intensive tasks such as question answering. To address
  this challenge, we introduce REFIND (Retrieval-augmented Factuality
  hallucINation Detection), a novel framework that detects hallucinated spans
  within LLM outputs by directly leveraging retrieved documents. As part of the
  REFIND, we propose the Context Sensitivity Ratio (CSR), a novel metric that
  quantifies the sensitivity of LLM outputs to retrieved evidence. This
  innovative approach enables REFIND to efficiently and accurately detect
  hallucinations, setting it apart from existing methods. In the evaluation,
  REFIND demonstrated robustness across nine languages, including low-resource
  settings, and significantly outperformed baseline models, achieving superior
  IoU scores in identifying hallucinated spans. This work highlights the
  effectiveness of quantifying context sensitivity for hallucination detection,
  thereby paving the way for more reliable and trustworthy LLM applications
  across diverse languages.
paper_authors._id: 67b69cf4573aa8417aec103c
paper_authors.name: DongGeon Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-20T09:35:55.480Z
paper_authors._id: 6540fbf9cb7fffd683942b43
paper_authors.avatarUrl: /avatars/d4a64fbde511d0949e1c339179586850.svg
paper_authors.fullname: DongGeon Lee
paper_authors.type: user
paper_authors.user: oneonlee
paper_authors._id: 67b69cf4573aa8417aec103d
paper_authors.name: Hwanjo Yu
paper_title: REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large
    Language Models
paper_summary: Hallucinations in large language model (LLM) outputs severely limit their
  reliability in knowledge-intensive tasks such as question answering. To address
  this challenge, we introduce REFIND (Retrieval-augmented Factuality
  hallucINation Detection), a novel framework that detects hallucinated spans
  within LLM outputs by directly leveraging retrieved documents. As part of the
  REFIND, we propose the Context Sensitivity Ratio (CSR), a novel metric that
  quantifies the sensitivity of LLM outputs to retrieved evidence. This
  innovative approach enables REFIND to efficiently and accurately detect
  hallucinations, setting it apart from existing methods. In the evaluation,
  REFIND demonstrated robustness across nine languages, including low-resource
  settings, and significantly outperformed baseline models, achieving superior
  IoU scores in identifying hallucinated spans. This work highlights the
  effectiveness of quantifying context sensitivity for hallucination detection,
  thereby paving the way for more reliable and trustworthy LLM applications
  across diverse languages.
paper_ai_keywords: retrieval-augmented factuality hallucINation detection hallucinations LLM outputs retrieved documents Context Sensitivity Ratio (CSR) sensitivity evidence hallucination detection robustness low-resource settings IoU scores