In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the prompt. However, trustworthy issues with LLM’s response, such as hallucination, have also been actively discussed. Existing works have been devoted to quantifying the uncertainty in LLM’s response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning. In this work, we delve into the predictive uncertainty of LLMs associated with in-context learning, highlighting that such uncertainties may stem from both the provided demonstrations (aleatoric uncertainty) and ambiguities tied to the model’s configurations (epistemic uncertainty). We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. Extensive experiments are conducted to demonstrate the effectiveness of the decomposition. The code and data are available at: https://github.com/lingchen0331/UQ_ICL.

“近年来,电信网络诈骗形势较为严峻,自动化案件分类有助于打击犯罪。本文介绍了任务相关的分类体系,其次从数据集、任务介绍、比赛结果等方面介绍并展示了本次评测任务的相关信息。本次任务共有60支参赛队伍报名,最终有34支队伍提交结果,其中有15支队伍得分超过 baseline,最高得分为0.8660,高于baseline 1.6%。根据结果分析,大部分队伍均采用了BERT类模型。”