MACI: Multi-LLM Adaptive Conformal Inference

This is the official repository for the paper "Multi-LLM Adaptive Conformal Inference for Reliable LLM Responses".

📄 Paper: arXiv:2602.01285
💻 Code: GitHub Repository

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.

Usage

Please refer to our GitHub Repository for installation and usage instructions.