Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.20673 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914439065763840 |
|---|---|
| author | Huang, Tianyi Yang, Caden Yin, Emily Wang, Eric Zhang, Michael |
| author_facet | Huang, Tianyi Yang, Caden Yin, Emily Wang, Eric Zhang, Michael |
| contents | 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 retriever and backbone, PAVE outperforms simpler post-retrieval baselines in two evidence-grounded QA settings, with the largest gain reaching 32.7 accuracy points on a span-grounded benchmark. We view these findings as proof-of-concept evidence that explicit premise extraction plus support-gated revision can strengthen evidence-grounded consistency in retrieval-augmented LLM systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20673 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs Huang, Tianyi Yang, Caden Yin, Emily Wang, Eric Zhang, Michael Computation and Language Artificial Intelligence 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 retriever and backbone, PAVE outperforms simpler post-retrieval baselines in two evidence-grounded QA settings, with the largest gain reaching 32.7 accuracy points on a span-grounded benchmark. We view these findings as proof-of-concept evidence that explicit premise extraction plus support-gated revision can strengthen evidence-grounded consistency in retrieval-augmented LLM systems. |
| title | PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2603.20673 |