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This research proposes BeyondUncertainty, a method that leverages an LLM's internal confidence estimates to intelligently route queries between direct answering and retrieval-augmented generation. By filtering out high-confidence questions from the search process, the approach reduces unnecessary token consumption by over 20% while improving overall answer accuracy on multiple benchmarks. The study highlights a critical trade-off in RAG systems: using model self-knowledge can optimize efficiency without sacrificing performance, though it does incur additional computational cost for generating those confidence probes.
Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs
arXiv:2607.25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation. We investigate whether verbalized confidence from black-box language models can serve as an actionable signal for retrieval routing. Our method, BeyondUncertainty, first elicits a structured provisional answer and confidence estimate, then applies a model-specific threshold selected on held-out validation data and frozen before test evaluation. Low-confidence questions receive top-$5$ TF--IDF retrieval followed by a second answer call, whereas high-confidence questions return the provisional answer directly. We evaluate 27,000 policy instances across six QA benchmarks, three model families, and three retrieval policies. BeyondUncertainty achieves $0.483$ mean token-level F1, compared with $0.467$ for always retrieval and $0.401$ for no retrieval, while reducing retrieved passages by $20.4\%$ relative to always retrieval. When matched on the number of questions routed to retrieval within each dataset--model cell, it outperforms a post-hoc random allocation in 17 of 18 settings, with an average gain of 0.024 F1. Although poorly calibrated as an absolute probability, probe uncertainty modestly predicts question-level retrieval benefit (AUROC $=0.628$). However, the additional probe increases total token usage by $28.2\%$, revealing a trade-off between more selective evidence acquisition and end-to-end token efficiency.
Abstract:Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation. We investigate whether verbalized confidence from black-box language models can serve as an actionable signal for retrieval routing. Our method, BeyondUncertainty, first elicits a structured provisional answer and confidence estimate, then applies a model-specific threshold selected on held-out validation data and frozen before test evaluation. Low-confidence questions receive top-$5$ TF--IDF retrieval followed by a second answer call, whereas high-confidence questions return the provisional answer directly. We evaluate 27,000 policy instances across six QA benchmarks, three model families, and three retrieval policies. BeyondUncertainty achieves $0.483$ mean token-level F1, compared with $0.467$ for always retrieval and $0.401$ for no retrieval, while reducing retrieved passages by $20.4\%$ relative to always retrieval. When matched on the number of questions routed to retrieval within each dataset--model cell, it outperforms a post-hoc random allocation in 17 of 18 settings, with an average gain of 0.024 F1. Although poorly calibrated as an absolute probability, probe uncertainty modestly predicts question-level retrieval benefit (AUROC $=0.628$). However, the additional probe increases total token usage by $28.2\%$, revealing a trade-off between more selective evidence acquisition and end-to-end token efficiency.
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2607.25600 [cs.IR]
(or arXiv:2607.25600v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2607.25600 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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