Knowledge Dependency Estimation for Reliable Question Answering
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 stru...
| Authors | Chaodong Tong, Qi Zhang, Nannan Sun et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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