归因与对齐：局部上下文重复对对话中话语生成与理解的影响
Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue
AI总结：
本研究探讨了局部上下文重复在对话中的作用，评估了语言模型在话语生成中是否产生人类水平的重复，以及在理解时使用的词汇重用机制，为开发受认知启发的对话生成系统提供参考。
摘要：
语言模型常被用作现代对话系统的骨干。这些模型在海量流畅的书面语言上进行预训练。在评估语言模型的生成时，重复通常会受到惩罚。然而，重复是对话的关键组成部分。人类使用局部和特定于对话伙伴的重复；这些重复受到人类用户的青睐，并能在对话中促成更成功的交流。在本研究中，我们评估：语言模型在对话中是否产生人类水平的重复，以及它们在理解过程中使用了哪些与词汇重用相关的处理机制。我们认为，这种对模型生成与理解行为的联合分析，能为受认知启发的对话生成系统的开发提供参考。
英文摘要：
Language models are often used as the backbone of modern dialogue systems. These models are pre-trained on large amounts of written fluent language. Repetition is typically penalised when evaluating language model generations. However, it is a key component of dialogue. Humans use local and partner specific repetitions; these are preferred by human users and lead to more successful communication in dialogue. In this study, we evaluate (a) whether language models produce human-like levels of repetition in dialogue, and (b) what are the processing mechanisms related to lexical re-use they use during comprehension. We believe that such joint analysis of model production and comprehension behaviour can inform the development of cognitively inspired dialogue generation systems.
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。
