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Conformal Prediction with Large Language Models for Multi-Choice Question Answering
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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.
Forward citations
Cited by 15 Pith papers
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Conformal Arbitrage: Risk-Controlled Balancing of Competing Objectives in Language Models
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CP-Router: An Uncertainty-Aware Router Between LLM and LRM
CP-Router uses conformal prediction set sizes from an LLM to decide whether to route a prompt to that LLM or to a more expensive reasoning model, cutting token use with minimal or no accuracy loss.
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WQLCP: Weighted Adaptive Conformal Prediction for Robust Uncertainty Quantification Under Distribution Shifts
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A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions
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