Multi-LLM Adaptive Conformal Inference for Reliable LLM Responses
Abstract
Ensuring factuality is essential for the safe use of Large Language Models (LLMs) in high-stakes domains such as medicine and law. Conformal inference provides distribution-free guarantees, but existing approaches are either overly conservative, discarding many true-claims, or rely on adaptive error rates and simple linear models that fail to capture complex group structures. To address these challenges, we reformulate conformal inference in a multiplicative filtering setting, modeling factuality as a product of claim-level scores. Our method, Multi-LLM Adaptive Conformal Inference (MACI), leverages ensembles to produce more accurate factuality-scores, which in our experiments led to higher retention, while validity is preserved through group-conditional calibration. Experiments show that MACI consistently achieves user-specified coverage with substantially higher retention and lower time cost than baselines. Our repository is available at https://github.com/MLAI-Yonsei/MACI
Cite
@article{arxiv.2602.01285,
title = {Multi-LLM Adaptive Conformal Inference for Reliable LLM Responses},
author = {Kangjun Noh and Seongchan Lee and Ilmun Kim and Kyungwoo Song},
journal= {arXiv preprint arXiv:2602.01285},
year = {2026}
}
Comments
Accepted to ICLR 2026