用多模型融合提升大模型回答可靠性，保真同时减少误删。
Multi-LLM Adaptive Conformal Inference for Reliable LLM Responses
- 通过多模型集成生成更精准的可信度评分
- 在保证覆盖率前提下，比基线保留更多正确答案，耗时更低
- 适合医疗、法律等对准确性要求高的场景
确保事实性对于大语言模型在医疗、法律等高风险领域的安全应用至关重要。置信推断提供无需分布假设的保证，但现有方法或过于保守（丢弃大量真实陈述），或依赖简单线性模型与自适应误差率，难以捕捉复杂群体结构。本文将置信推断重新建模为乘法过滤框架，将事实性建模为命题级得分的乘积。提出多大模型自适应置信推断（MACI）方法，利用集成学习生成更准确的事实性评分，在实验中实现更高保留率，同时通过组条件校准保持有效性。结果表明，MACI在满足用户指定覆盖率的同时，显著提升保留率并降低时间成本，优于基线方法。代码已开源：https://github.com/MLAI-Yonsei/MACI。
原文摘要 · Abstract (English)
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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