为大模型生成结果提供可信赖的置信度评估方法
COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generation
- 将真实答案显式加入候选输出，提升预测集覆盖性
- 在6个大模型、4类任务上实现误差率精准控制
- 适合需要可靠输出评估的生成应用开发者
自然语言生成中的不确定性量化对评估大模型性能至关重要，可揭示预测置信度、识别失效模式并衡量输出可靠性。虽然校准预测（CP）方法在分类任务中表现良好，但在自然语言生成中，基于采样的候选输出难以保证包含真实答案，限制了其在不同误差率下的适用性。为此，我们提出一种新方法：将真实答案显式加入候选集，并利用对数几率分数衡量非符合性。在六个大模型和四个自然语言生成任务上的实验表明，该方法在误差率校准和实际覆盖率方面均优于基线，在用户指定的各种误差率下均能实现准确的不确定性量化。
原文摘要 · Abstract (English)
Uncertainty Quantification (UQ) for Natural Language Generation (NLG) is crucial for assessing the performance of Large Language Models (LLMs), as it reveals confidence in predictions, identifies failure modes, and gauges output reliability. Conformal Prediction (CP), a model-agnostic method that generates prediction sets with a specified error rate, has been adopted for UQ in classification tasks, where the size of the prediction set indicates the model's uncertainty. However, when adapting CP to NLG, the sampling-based method for generating candidate outputs cannot guarantee the inclusion of the ground truth, limiting its applicability across a wide range of error rates. To address this, we propose \ourmethod, a method that explicitly adds the ground truth to the candidate outputs and uses logit scores to measure nonconformity. Our experiments with six LLMs on four NLG tasks show that \ourmethod outperforms baseline methods in calibrating error rates and empirical cover rates, offering accurate UQ across a wide range of user-specified error rates.
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