SAFE：用 LLM 验证器实现证据支撑的多跳推理框架
SAFE: An LLM-as-Verifier Framework for Evidence-Grounded Multi-Hop Reasoning
AI 导读
论文提出 SAFE，一个以 LLM 作为验证器的框架，用于证据支撑的多跳问答。该方法将推理分解为基于 Knowledge Graph（KG）三元组的原子证据单元，在生成过程中逐步校验中间步骤并在错误传播前给出纠正反馈，训练时则在 KG 约束下构建可靠的验证器训练数据。在三个多跳问答基准上，SAFE 平均提升准确率 8.8 pp，论文已被 EMNLP 2026 主会接收。
正文 · 原文
Abstract:Multi-hop QA benchmarks often reward Large Language Models (LLMs) for spurious correctness, where models reach correct answers through invalid intermediate reasoning. We propose SAFE, an LLM-as-verifier framework for evidence-grounded multi-hop QA. Rather than judging only the final answer after generation, SAFE verifies reasoning during generation by checking intermediate steps against the provided passages and previous reasoning trajectory. To make this process checkable, SAFE decomposes reasoning into atomic, evidence-grounded units represented with Knowledge Graph (KG) triples. At train-time, SAFE verifies benchmark supervision under KG-grounded constraints and constructs reliable verifier training data. At inference-time, an external verifier checks each generated step, identifies invalid reasoning, and provides correction feedback before errors propagate. Across three multi-hop QA benchmarks, SAFE improves accuracy by 8.8 pp on average. These results show that evidence-grounded multi-hop QA benefits from shifting LLM-based evaluation from post-hoc answer judgment to stepwise reasoning verification.
| Comments: | Accepted to EMNLP 2026 (Main Conference). Github: this https URL | Dataset: this https URL |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2604.01993 [cs.CL] |
| (or arXiv:2604.01993v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.01993 arXiv-issued DOI via DataCite |
Submission history
From: Daeyong Kwon [view email]
[v1]
Thu, 2 Apr 2026 12:59:30 UTC (2,777 KB)
[v2]
Tue, 9 Jun 2026 17:29:55 UTC (2,397 KB)
[v3]
Sun, 23 Aug 2026 03:17:47 UTC (2,397 KB)
[v4]
Thu, 29 Oct 2026 06:25:32 UTC (2,398 KB)
来源：arXiv cs.AI · arxiv.org