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.

这篇实验研究分析了信息寻求对话中大型语言模型在分类明示或隐含对话转换以及预测对接知识元素方面的能力，并揭示了大型语言模型在这两个任务中遇到的挑战，讨论了通过流程架构和知识库改进大型语言模型的对话接地理解的研究工作，在对话中处理对接知识的复杂性方面，旨在开发更加有效的对话系统。