PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs
Retrieval-augmented language models can retrieve relevant evidence yet still commit to answers before explicitly checking whether the retrieved context supports the conclusion. We present PAVE (Premise-Grounded Answer Validation and Editing), an inference-time validation layer for evidence-grounded question answering. PAVE decomposes retrieved context into question-conditioned atomic facts, drafts an answer, scores how well that draft is supported by the extracted premises, and revises low-support outputs before finalization. The resulting trace makes answer commitment auditable at the level of explicit premises, support scores, and revision decisions. In controlled ablations with a fixed...
| Authors | Tianyi Huang, Caden Yang, Emily Yin et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 March 2026 |
| Last updated at source | 2 April 2026 |
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