梳理大模型不确定性估计四大方法，助你识别幻觉与偏见。
A Survey of Uncertainty Estimation Methods on Large Language Models
- 从四个方向系统整理大模型不确定性评估方法
- 跨多数据集验证多种方法有效性，提供实证依据
- 适合关注模型可信性、安全性的研究者与开发者
大语言模型在诸多任务中展现出强大能力，但其输出可能包含偏见、幻觉或非事实内容，且常以流畅自然的外观掩盖问题。不确定性估计是应对这一挑战的关键方法。尽管相关研究日益增多，目前仍缺乏针对大模型不确定性估计的系统性综述。本文梳理了大模型不确定性估计的四大主要方向，并在多个方法和数据集上进行了广泛实验评估。最后，提出了具有批判性和前景的未来研究方向。
原文摘要 · Abstract (English)
Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, these models could offer biased, hallucinated, or non-factual responses camouflaged by their fluency and realistic appearance. Uncertainty estimation is the key method to address this challenge. While research efforts in uncertainty estimation are ramping up, there is a lack of comprehensive and dedicated surveys on LLM uncertainty estimation. This survey presents four major avenues of LLM uncertainty estimation. Furthermore, we perform extensive experimental evaluations across multiple methods and datasets. At last, we provide critical and promising future directions for LLM uncertainty estimation.
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