Knowledge Dependency Estimation for Reliable Question Answering

• URL: http://arxiv.org/abs/2605.28047v1
• Date: Wed, 27 May 2026 06:48:57 GMT
• Title: Knowledge Dependency Estimation for Reliable Question Answering
• Abstract summary: We study emphknowledge dependency estimation<n>We propose textbfKnot, a structured rank-aware knowledge dependency estimator.
• Score: 11.800158247203312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable question answering requires identifying not only whether an answer is correct, but also which available knowledge the prediction depends on. In realistic LLM-based QA, this knowledge may come from context, retrieval, decomposition, or intermediate reasoning, forming a noisy and redundant candidate space rather than a clean gold evidence set. We study \emph{knowledge dependency estimation}: estimating the sensitivity of a fixed black-box QA model to different candidate knowledge units. The challenge is to obtain fine-grained dependency scores without exhaustive test-time perturbation while modeling redundancy, substitutability, and complementarity. We propose \textbf{Knot}, a structured rank-aware knowledge dependency estimator. Knot learns from subset-level counterfactual supervision, models subset sensitivity through coverage over latent dependency factors, and derives rank-aware unit scores to identify influential candidates. Across multiple-choice and generative QA benchmarks, Knot outperforms all compared baselines in subset-sensitivity prediction and produces more faithful unit rankings than deployable baselines without extra QA-model calls; when used for practical risk screening, its dependency scores help flag error-prone QA predictions early.

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