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.

翻译：语言模型常被用作现代对话系统的基础架构。这些模型在大量书面流畅语言数据上进行预训练。在评估语言模型生成的文本时，重复通常会被惩罚。然而，重复是对话的关键组成部分。人类会使用局部和对话者特定的重复模式，这些重复受到人类用户的偏好，并能在对话中促成更成功的交流。在本研究中，我们评估了：（a）语言模型在对话中是否产生类人水平的重复现象，以及（b）它们在理解过程中与词汇复用相关的加工机制。我们相信，这种对模型产出和理解行为的联合分析，能够为认知启发的对话生成系统的开发提供启示。