Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction

Harit Vishwakarma, Alan Mishler, Thomas Cook, Niccolo Dalmasso, Natraj Raman, Sumitra Ganesh

Keywords: Large Language Models, Conformal Prediction, Uncertainty Quantification, Prompting, MCQ, Tool Learning, Agentic AI, Test-time Scaling

TL;DR: We propose CROQ, which improves LLM accuracy by refining MCQ answer choices using conformal prediction. To enhance CROQ, we introduce CP-OPT, optimizing scores for smaller prediction sets. Experiments show CROQ is effective, especially with CP-OPT.

Abstract:

Large language models (LLMs) are empowering decision-making in several applications, including tool or API usage and answering multiple-choice questions (MCQs). However, incorrect outputs pose significant risks in high-stakes domains like healthcare and finance. To quantify LLM uncertainty and thereby mitigate these risks, recent works employ conformal prediction (CP), a model- and distribution-agnostic framework that uses LLM outputs to generate a \emph{prediction set} containing the true answer with high probability. Leveraging CP, we propose \emph{conformal revision of questions} (CROQ), which revises the question by narrowing down the available choices to those in the prediction set and asking the LLM the revised question. We expect LLMs to be more accurate on revised questions with fewer choices. Furthermore, we expect CROQ to be effective when the prediction sets from CP are small. Commonly used logit scores often lead to large sets, diminishing CROQ's effectiveness. To overcome this, we propose CP-OPT, an optimization framework to learn scores that minimize set sizes while maintaining coverage. Our extensive experiments on MMLU, ToolAlpaca, and TruthfulQA datasets with multiple LLMs show that CROQ improves accuracy over the standard inference, with more pronounced gains when paired with CP-OPT.

Submission Number: 34