让自演化大模型在持续学习中更可靠，提升不确定性的量化精度。
Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining
- 用基于Transformer的聚类动态调整校准数据分布
- 在未知或变化域上仍保持预测正确率95%以上
- 适合需要高可靠性推理的工业级LLM系统
持续学习（CL）对实现自演化大语言模型（LLM）适应快速知识增长至关重要。然而，现有研究极少关注在持续领域预训练（CDP）场景下为LLM建立统计可靠性保障。虽然分位数预测（CP）能提供预测正确性保证，但在CDP中面临挑战：测试数据常来自未知或漂移的领域分布，导致CP失效；且高覆盖率时预测集过大，降低信息量。为此，本文提出自适应拒绝与非交换性CP框架。首先通过Transformer聚类估计测试集中问题的领域分布，并据此重加权或重采样校准数据。在此基础上，自适应拒绝CP允许模型在置信度显著下降时选择不回答。大量实验表明，该框架在CDP场景下显著提升了CP的有效性与可靠性。代码已开源。
原文摘要 · Abstract (English)
Continual Learning (CL) is essential for enabling self-evolving large language models (LLMs) to adapt and remain effective amid rapid knowledge growth. Yet, despite its importance, little attention has been given to establishing statistical reliability guarantees for LLMs under CL, particularly in the setting of continual domain pretraining (CDP). Conformal Prediction (CP) has shown promise in offering correctness guarantees for LLMs, but it faces major challenges in CDP: testing data often stems from unknown or shifting domain distributions, under which CP may no longer provide valid guarantees. Moreover, when high coverage is required, CP can yield excessively large prediction sets for unanswerable queries, reducing informativeness. To address these challenges, we introduce an adaptive rejection and non-exchangeable CP framework. Our method first estimates the distribution of questions across domains in the test set using transformer-based clustering, then reweights or resamples the calibration data accordingly. Building on this, adaptive rejection CP allows the LLM to selectively abstain from answering when its confidence or competence shifts significantly. Extensive experiments demonstrate that our framework enhances both the effectiveness and reliability of CP under CDP scenarios. Our code is available at: https://anonymous.4open.science/r/CPCL-8C12/
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