Multi-LLM Adaptive Conformal Inference for Reliable LLM Responses

• URL: http://arxiv.org/abs/2602.01285v1
• Date: Sun, 01 Feb 2026 15:34:45 GMT
• Title: Multi-LLM Adaptive Conformal Inference for Reliable LLM Responses
• Authors: Kangjun Noh, Seongchan Lee, Ilmun Kim, Kyungwoo Song,
• Abstract summary: We reformulate conformal inference in a multiplicative filtering setting, modeling factuality as a product of claim-level scores.<n>Our method, Multi-LLM Adaptive Conformal Inference (MACI), leverages ensembles to produce more accurate factuality-scores.<n>Experiments show that MACI consistently achieves user-specified coverage with substantially higher retention and lower time cost than baselines.
• Score: 18.60553322553765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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