REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large
  Language Models
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.
67b69cf4573aa8417aec103c
DongGeon Lee
claimed_verified
2025-02-20T09:35:55.480Z
6540fbf9cb7fffd683942b43
/avatars/d4a64fbde511d0949e1c339179586850.svg
DongGeon Lee
user
oneonlee
67b69cf4573aa8417aec103d
Hwanjo Yu
REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large
  Language Models
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.
REFIND, a novel retrieval-augmented framework, detects hallucinations in large language model outputs by leveraging retrieved documents and quantifying context sensitivity, outperforming baselines across multiple languages.
large language model REFIND Retrieval-augmented Factuality hallucINation Detection hallucinations Context Sensitivity Ratio CSR context sensitivity low-resource IoU scores