Retromorphic Testing with Hierarchical Verification for Hallucination Detection in RAG
Large language models can still hallucinate in retrieval-augmented generation (RAG), producing claims that are unsupported by or conflict with the retrieved context. Detecting such errors remains challenging when faithfulness is judged solely against the retrieved context: many existing detectors return holistic answer-level scores, while others target open-domain factuality or fail to provide evidence-grounded diagnostics. We present RT4CHART, a retromorphic testing framework for context-faithfulness assessment. RT4CHART decomposes an answer into independently verifiable claims, performs hierarchical local-to-global verification against the retrieved context, and assigns each claim one o...
| Authors | Boxi Yu, Yuzhong Zhang, Liting Lin et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 March 2026 |
| Last updated at source | 20 July 2026 |
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