Facet-Level Tracing of Evidence Uncertainty and Hallucination in RAG
Retrieval-Augmented Generation (RAG) aims to reduce hallucination by grounding answers in retrieved evidence, yet hallucinated answers remain common even when relevant documents are available. Existing evaluations focus on answer-level or passage-level accuracy, offering limited insight into how evidence is used during generation. In this work, we introduce a facet-level diagnostics framework for QA that decomposes each input question into atomic reasoning facets. For each facet, we assess evidence sufficiency and grounding using a structured Facet x Chunk matrix that combines retrieval relevance with natural language inference-based faithfulness scores. To diagnose evidence usage, we ana...
| Authors | Passant Elchafei, Monorama Swain, Shahed Masoudian et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 April 2026 |
| Last updated at source | 19 May 2026 |
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