面向多模态多方对话中受话人识别的 LLM 基准
An LLM Benchmark for Addressee Recognition in Multi-modal Multi-party Dialogue
AI总结：
本文构建三人多模态对话语料并标注受话人信息，以 GPT-4o 建立受话人识别基准，证明其准确率仅略高于随机水平。
摘要：
处理多方对话是推进口语对话系统的重要一步，这就需要开发专门针对多方交互的任务。为应对这一挑战，我们正在构建一个包含三人（三名参与者）讨论的多模态多方对话语料库。本文聚焦受话人识别任务，即识别谁是下一话轮的受话对象，这是多方对话系统所特有的关键组成部分。语料库的一个子集被标注了受话人信息，结果显示约 20% 的对话话轮中会明确指出受话人。为评估该任务的复杂性，我们对大型语言模型 GPT-4o 在受话人识别上的表现进行了基准测试。结果表明，GPT-4o 达到的准确率仅略高于随机水平，凸显了多方对话中受话人识别的挑战。这些发现说明，需要进一步研究以提升大型语言模型理解并应对多方对话动态复杂性的能力。
英文摘要：
Handling multi-party dialogues represents a significant step for advancing spoken dialogue systems, necessitating the development of tasks specific to multi-party interactions. To address this challenge, we are constructing a multi-modal multi-party dialogue corpus of triadic (three-participant) discussions. This paper focuses on the task of addressee recognition, identifying who is being addressed to take the next turn, a critical component unique to multi-party dialogue systems. A subset of the corpus was annotated with addressee information, revealing that explicit addressees are indicated in approximately 20% of conversational turns. To evaluate the task's complexity, we benchmarked the performance of a large language model (GPT-4o) on addressee recognition. The results showed that GPT-4o achieved an accuracy only marginally above chance, underscoring the challenges of addressee recognition in multi-party dialogue. These findings highlight the need for further research to enhance the capabilities of large language models in understanding and navigating the intricacies of multi-party conversational dynamics.
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