标题： 不要弃权！ 识别并解决不确定性 显示英文标题

标题： Do not Abstain! Identify and Solve the Uncertainty

摘要： 尽管大型语言模型（LLMs）已在各个领域得到广泛应用，但它们在面对不确定场景时常常表现出过度自信，而现有的解决方案主要依赖于回避性回答（例如，“我不知道”），这忽略了识别和解决不确定性以生成更令人满意的回答的机会。为了系统地研究和提升LLMs识别和解决不确定性来源的能力，我们引入了\textbf{ConfuseBench}，这是一个主要关注三种不确定性类型的基准：文档稀缺性、能力限制和查询模糊性。通过ConfuseBench的实验表明，当前的LLMs难以准确识别不确定性根源并加以解决。它们倾向于将不确定性归因于查询模糊性，而忽视了能力限制，尤其是对于较弱的模型来说更是如此。为了解决这一挑战，我们首先生成了上下文感知的查询，以突出原始查询中的困惑之处。然后根据查询答案的独特性判断不确定性来源。进一步使用了一种基于策略的训练方法InteractDPO来生成更好的查询。实验结果证明了我们的方法的有效性。 显示英文摘要

摘要： Despite the widespread application of Large Language Models (LLMs) across various domains, they frequently exhibit overconfidence when encountering uncertain scenarios, yet existing solutions primarily rely on evasive responses (e.g., "I don't know") overlooks the opportunity of identifying and addressing the uncertainty to generate more satisfactory responses. To systematically investigate and improve LLMs' ability of recognizing and addressing the source of uncertainty, we introduce \textbf{ConfuseBench}, a benchmark mainly focus on three types of uncertainty: document scarcity, limited capability, and query ambiguity. Experiments with ConfuseBench reveal that current LLMs struggle to accurately identify the root cause of uncertainty and solve it. They prefer to attribute uncertainty to query ambiguity while overlooking capability limitations, especially for those weaker models. To tackle this challenge, we first generate context-aware inquiries that highlight the confusing aspect of the original query. Then we judge the source of uncertainty based on the uniqueness of the inquiry's answer. Further we use an on-policy training method, InteractDPO to generate better inquiries. Experimental results demonstrate the efficacy of our approach.