MiRD: Reliable Set-Valued Prediction for Open-Ended Question Answering via Miscoverage Risk Decomposition
arXiv:2605.27091v1 Announce Type: new Abstract: Reliable set-valued prediction provides a principled way to mitigate hallucinations in open-ended question answering (QA), yet existing conformal approaches typically rely on a fragile premise: finite sampling must already produce at least one admissible candidate, or calibration examples violating this condition are discarded. In this paper, we introduce MiRD, a two-stage framework that decomposes overall miscoverage into sampling failure and conditional selection failure. In Stage I, MiRD establishes an expectation-level marginal upper bound on
The proliferation of open-ended AI question answering systems increases the urgency for robust hallucination mitigation techniques.
This development offers a principled approach to improving the reliability and safety of advanced AI systems, particularly in critical applications.
Current, often 'fragile,' conformal AI approaches are being replaced by more rigorous methods that better quantify and decompose prediction risks.
- · AI developers
- · Enterprises deploying AI
- · Research institutions
- · AI systems prone to hallucination
- · Organizations relying on uncalibrated AI
Increased trust and adoption of AI in sensitive applications due to improved reliability.
Reduced incidence of AI-induced errors and their corresponding economic or social costs.
Acceleration of AI integration into white-collar workflows as reliability concerns diminish, potentially impacting the 'AI Agents' narrative.
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Read at arXiv cs.CL