# MiRD: Reliable Set-Valued Prediction for Open-Ended Question Answering via Miscoverage Risk Decomposition

arXiv: https://arxiv.org/abs/2605.27091 · HTML: https://mlsanity.com/paper/2605.27091/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 25 May 2026.

## Builds on (most cited first, 9 of 9)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Conformal Prediction with Large Language Models for Multi-Choice Question Answering](https://mlsanity.com/paper/2305.18404/) (2023)
- [Theoretical Foundations of Conformal Prediction](https://mlsanity.com/paper/2411.11824/) (2024)
- [LEC: Linear Expectation Constraints for Selection-Conditioned Risk Control in Selective Prediction and Routing Systems](https://mlsanity.com/paper/2512.01556/) (2025)
- [COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generation](https://mlsanity.com/paper/2502.12601/) (2025)
- [Conformal Lesion Segmentation for 3D Medical Images](https://mlsanity.com/paper/2510.17897/) (2025)
- [Set-Valued Prediction for Large Language Models with Feasibility-Aware Coverage Guarantees](https://mlsanity.com/paper/2603.22966/) (2026)
- [BalanceRAG: Joint Risk Calibration for Cascaded Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.20084/) (2026)

## Built on by (most cited first, 1 of 1)

- [Asymptotic Risk Calibration for Selective Question Answering](https://mlsanity.com/paper/2608.12008/) (2026)

## Abstract (arXiv)

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 probability that finite sampling produces no admissible answer under a fixed budget. In Stage II, conditioned on sampling success, MiRD calibrates a conformal selection threshold using admission-correlated nonconformity scores defined over the full calibration set, thereby preserving calibration-set integrity. Across three open-ended QA datasets and eight models, MiRD controls sampling risk, conditional selection risk, and overall miscoverage, while yielding tighter first-stage bounds than PAC-style alternatives and more adaptive prediction sets than successful-only calibration.