arXiv:2605.25133v1 Announce Type: cross Abstract: Reliably knowing when a language model is correct is almost as important as being correct. We introduce prover-verifier deliberation (PVD), an inference-time protocol grounded in interactive proof theory, as a mechanism for selective prediction: the protocol produces both an answer and a structured confidence verdict, allowing a system to report high-confidence answers while abstaining on uncertain cases. In each dialogue, a prover defends a candidate answer through checkable sub-claims while a verifier issues targeted challenges and returns \t
The rapid deployment of LLMs highlights the critical need for improved reliability and selective prediction mechanisms, driving research into methods like prover-verifier deliberation to enhance trust.
Reliably knowing when an AI is correct is crucial for deploying LLMs in high-stakes environments, making this development foundational for broader AI integration and trust.
Language models will gain a built-in mechanism for self-assessment and explicit confidence reporting, moving beyond black-box predictions to verifiable answers.
- · AI developers
- · High-stakes AI applications
- · Users concerned about AI accuracy
- · AI models lacking confidence mechanisms
- · Applications demanding 100% accuracy without verification
- · Those relying solely on raw LLM output
LLMs can selectively abstain from answering questions where they lack confidence, improving overall system reliability.
This framework could lead to more robust and auditable AI systems, fostering greater public and institutional trust.
The concept of 'interactive proof theory' could extend beyond LLMs, becoming a standard for verifiable AI across different modalities.
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Read at arXiv cs.CL