针对处理矛盾对话的语言模型红队测试
Red Teaming Language Models for Processing Contradictory Dialogues
AI总结：
针对语言模型对话易自相矛盾的问题，本研究构建带解释标签的矛盾对话数据集，提出红队测试框架，通过检测、解释、修改的流程提升矛盾处理能力，凸显对话AI逻辑一致性的重要性。
摘要：
当前大多数可用语言模型在对话过程中容易出现自相矛盾的问题。为缓解这一问题，本研究探索了一项全新的矛盾对话处理任务，旨在检测并修改对话中的矛盾陈述。该任务受上下文忠实度与对话理解相关研究的启发，这些研究表明，矛盾的检测与理解通常需要详细的解释支撑。我们构建了一个包含矛盾对话的数据集，其中对话的一方会出现自相矛盾的情况，每段对话都配有解释性标签，标注出矛盾的位置与具体细节。基于该数据集，我们提出了一种用于矛盾对话处理的Red Teaming（红队测试）框架。该框架先检测对话并尝试给出解释，再利用解释修改已有的矛盾内容。我们的实验表明，该框架提升了矛盾对话的检测能力，且能提供有效的解释，同时在修改此类对话方面展现出了显著的能力。本研究凸显了对话人工智能中逻辑不一致问题的重要性。
英文摘要：
Most language models currently available are prone to self-contradiction during dialogues. To mitigate this issue, this study explores a novel contradictory dialogue processing task that aims to detect and modify contradictory statements in a conversation. This task is inspired by research on context faithfulness and dialogue comprehension, which have demonstrated that the detection and understanding of contradictions often necessitate detailed explanations. We develop a dataset comprising contradictory dialogues, in which one side of the conversation contradicts itself. Each dialogue is accompanied by an explanatory label that highlights the location and details of the contradiction. With this dataset, we present a Red Teaming framework for contradictory dialogue processing. The framework detects and attempts to explain the dialogue, then modifies the existing contradictory content using the explanation. Our experiments demonstrate that the framework improves the ability to detect contradictory dialogues and provides valid explanations. Additionally, it showcases distinct capabilities for modifying such dialogues. Our study highlights the importance of the logical inconsistency problem in conversational AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
