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Conformal Prediction with Large Language Models for Multi-Choice Question Answering

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arxiv 2305.18404 v3 pith:VSR3DGPK submitted 2023-05-28 cs.CL cs.LGstat.ML

Bhawesh Kumar , Charlie Lu , Gauri Gupta , Anil Palepu , David Bellamy , Ramesh Raskar , Andrew Beam This is my paper
classification cs.CLcs.LGstat.ML
keywords predictionconformallanguagemodelslargeuncertaintyapplicationsquantification
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As large language models continue to be widely developed, robust uncertainty quantification techniques will become crucial for their safe deployment in high-stakes scenarios. In this work, we explore how conformal prediction can be used to provide uncertainty quantification in language models for the specific task of multiple-choice question-answering. We find that the uncertainty estimates from conformal prediction are tightly correlated with prediction accuracy. This observation can be useful for downstream applications such as selective classification and filtering out low-quality predictions. We also investigate the exchangeability assumption required by conformal prediction to out-of-subject questions, which may be a more realistic scenario for many practical applications. Our work contributes towards more trustworthy and reliable usage of large language models in safety-critical situations, where robust guarantees of error rate are required.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Improving Backward Conformal Prediction via Non-Conformity Score Transformation

    stat.ML 2026-02 reject novelty 7.0 of 10

    ST-BCP tightens the coverage bound in Backward Conformal Prediction by applying a computable data-dependent transformation to nonconformity scores, reducing the average gap from 4.20% to 1.12% on benchmarks while prov...

  2. Large Language Models for Statistical Inference: Context Augmentation with Applications to the Two-Sample Problem and Regression

    stat.ME 2025-06 conditional novelty 7.0 of 10

    Context augmentation uses LLM-generated contexts as latent variables to enable frequentist two-sample tests and text-on-text regression with claimed asymptotic guarantees.

  3. Cloud-Native Evaluation-as-a-Service: A Microservices Architecture for Scalable AI Monitoring with Conformal Guarantees

    cs.LG 2026-07 conditional novelty 5.0 of 10

    A reference architecture packages conformal prediction, calibration, drift detection, and fairness monitoring as six Kubernetes microservices, with experiments showing coverage and drift-detection behavior consistent ...

  4. Membership Inference Attacks with False Discovery Rate Control

    stat.ML 2025-08 conditional novelty 4.0 of 10

    A post-hoc wrapper, MIAFdR, converts any membership inference attack scores into conformal p-values and applies a Benjamini-Hochberg correction, guaranteeing that the expected proportion of non-members among flagged m...

  5. Shapley Uncertainty in Natural Language Generation

    cs.AI 2025-07 reject novelty 3.0 of 10

    A 'Shapley uncertainty' metric for LLM outputs is proposed, but its total equals the differential entropy it was meant to fix, and the claimed properties and performance gains are not supported.

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