Trust but Verify: Prover-Verifier Deliberation for Selective LLM Prediction
The paper introduces a new protocol called prover-verifier deliberation (PVD) for improving the reliability of language model predictions. This protocol allows a system to provide both answers and confidence levels, enabling it to report high-confidence responses while abstaining from uncertain ones. The authors empirically evaluate the protocol's effectiveness and highlight its potential advantages over existing methods.
- ▪Prover-verifier deliberation (PVD) is an inference-time protocol designed for selective prediction in language models.
- ▪The protocol produces both an answer and a structured confidence verdict, allowing for high-confidence reporting.
- ▪Empirical evaluations show that PVD can effectively separate reliable from unreliable answers.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.25133 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.25133 (cs) [Submitted on 24 May 2026] Title:Trust but Verify: Prover-Verifier Deliberation for Selective LLM Prediction Authors:João Sedoc, Baotong Zhang, Dean Foster View a PDF of the paper titled Trust but Verify: Prover-Verifier Deliberation for Selective LLM Prediction, by Jo\~ao Sedoc and 2 other authors View PDF HTML (experimental) Abstract:Reliably knowing when a language model is correct is almost as important as being correct.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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