探索利用大语言模型理解对话中的共识建立
Towards Harnessing Large Language Models for Comprehension of Conversational Grounding
AI总结：
本研究通过信息寻求对话实验，分析大语言模型在分类对话轮次和预测共识知识元素方面的能力，揭示其挑战，并提出利用流水线架构与知识库提升模型理解能力，以优化对话系统。
摘要：
对话共识建立是对话参与者之间确立共同知识的协作机制。本实验研究分析了信息寻求型对话，以调查大语言模型在分类与显式或隐式共识建立相关的对话轮次以及预测共识知识元素方面的能力。我们的实验结果揭示了大语言模型在这两项任务中面临的挑战，并讨论了通过流水线架构和知识库增强基于大语言模型的对话共识建立理解的持续研究工作。这些举措旨在开发更有效的对话系统，使其更好地处理对话中共识知识的复杂性。
英文摘要：
Conversational grounding is a collaborative mechanism for establishing mutual knowledge among participants engaged in a dialogue. This experimental study analyzes information-seeking conversations to investigate the capabilities of large language models in classifying dialogue turns related to explicit or implicit grounding and predicting grounded knowledge elements. Our experimental results reveal challenges encountered by large language models in the two tasks and discuss ongoing research efforts to enhance large language model-based conversational grounding comprehension through pipeline architectures and knowledge bases. These initiatives aim to develop more effective dialogue systems that are better equipped to handle the intricacies of grounded knowledge in conversations.
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。
