arXiv:2604.01993v2 Announce Type: replace 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 repres
The increasing complexity and unreliability of multi-hop reasoning in LLMs necessitate more robust verification frameworks, especially as these models are deployed in critical applications.
Improving the verifiability and trustworthiness of LLM outputs is crucial for their broader adoption and for preventing 'spurious correctness' from undermining their utility in complex tasks.
This framework shifts LLM evaluation from solely judging final answers to verifying intermediate reasoning steps, leading to more reliable and explainable AI outputs.
- · AI developers
- · Enterprises deploying LLMs
- · Users relying on LLM outputs
- · AI ethics and safety researchers
- · LLM models prone to hallucination
- · Developers neglecting reasoning verification
The quality and reliability of complex AI reasoning tasks will significantly improve.
This could accelerate the integration of LLMs into highly sensitive domains requiring high-assurance reasoning.
Increased trust in AI reasoning might reduce the need for human oversight in certain expert decision-making processes over time.
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Read at arXiv cs.CL