StepGap: A Hybrid NLI-LLM Checker for Step-Level Evidence-Gap Detectionin Multi-Hop Question Answering
StepGap introduces a structured approach to diagnosing failure modes in multi-hop reasoning systems by combining neural entailment classifiers with LLM decision trees to pinpoint three distinct error types: contradicted claims, irrelevant evidence, and missing reasoning bridges. The work exposes a critical blind spot in LLM-only checkers, where internal error cancellation masks individual component failures and inflates question-level metrics, suggesting that interpretability and decomposability matter more than raw performance parity when building reliable QA systems.
Modelwire context
ExplainerStepGap's real contribution isn't the hybrid architecture itself, but the discovery that LLM-only verifiers can produce correct final answers while masking broken intermediate steps. This means a system can look reliable on aggregate metrics while remaining fundamentally unreliable for deployment.
This connects directly to the compliance and auditing work from late May. The govllm framework (May 23) argued for continuous runtime monitoring rather than static certification, and RouteScan (May 24) showed how to detect behavioral drift in production. StepGap extends that logic to reasoning systems: you cannot trust a QA system's accuracy score alone. You need visibility into which component failed (contradiction, irrelevance, or missing bridge) to know whether the system is safe to deploy. Without that decomposition, you're flying blind on what will actually break in the field.
If StepGap's NLI-LLM hybrid outperforms LLM-only checkers on out-of-distribution multi-hop datasets (different domains, question types) that weren't in the training set, that validates the interpretability thesis. If it only wins on the original benchmark, the result is methodological rather than practically significant.
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