2025/02/19 by Donggeon Lee, Lee, DongGeon, Hwanjo Yu +1 · 1 citation
Medicine · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Data-Driven Disease Surveillance #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2502.13622
openalex publication_date 2025/02/19 · openalex created_date 2025/02/21 · openalex updated_date 2026/07/28
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. Our code is available at https://github.com/oneonlee/REFIND.