Conformal Prediction for Complex Fact-Checking with Large Language Models

Abstract

Large Language Models (LLMs) are increasingly deployed in high-stakes applications, where unreliable outputs can have serious consequences. However, quantifying their uncertainty and providing verifiable guarantees on their predictions remains an open challenge. We investigated the use of conformal prediction (CP), a statistical framework for generating prediction sets with provable coverage guarantees, to address this gap in LLM-based fact-checking. Using Llama-3.1-8B-Instruct and Mistral-7B-Instruct-v0.3, we evaluated three non-conformity scores on more than 10,000 claims verified by PolitiFact, spanning statements made between the website's launch in 2007 and January 2026. By formulating PolitiFact verdict classification as a multiple-choice question answering (MCQA) problem, we assessed the LLMs' confidence in classifying claims into PolitiFact's six "Truth-O-Meter" veracity ratings. We found that access to the corresponding fact-check article sharply reduced prediction set sizes, yielding sets small enough to be useful to a human fact-checker. Without this evidence, prediction sets typically contained both true and false labels, indicating that the models could not reliably distinguish true from false claims on their own at the 90% marginal coverage level. In this way, this research advances the application of LLMs in high-stakes contexts requiring verifiable bounds on error rates.