RARR Unraveled: Component-Level Insights into Hallucination Detection and Mitigation

Large Language Models (LLMs) often exhibit hallucinations, which makes detecting and mitigating these errors a critical challenge. The Retrofit Attribution using Research and Revision (RARR) framework addresses this challenge by extracting key aspects of an LLM response, verifying them against retrieved evidence, and resolving errors through re-prompting. In this work, we critically examine RARR and adapt its framework to incorporate publicly available evidence retrieval systems and generative models, thereby operationalizing the approach. We focus on hallucination detection, analyzing how each pipeline component contributes to this task. We also conduct a sentence-level analysis of hallucinations to provide a more granular assessment of RARR's performance. A key finding is that while query generation and retrieval are effective, the agreement module emerges as the weakest link in the RARR pipeline. We offer deeper insights into RARR's strengths, limitations, and potential areas for improvement, thereby broadening our understanding of hallucination detection in LLMs.