S3-DST：LLM 时代的结构化开放域对话分割与状态跟踪

摘要

传统的对话状态跟踪（DST）问题旨在跟踪用户偏好和意图在用户-代理对话中。尽管对于支持狭窄领域应用的任务导向型对话系统来说已经足够，但基于大型语言模型（LLM）的聊天系统的出现在开放域对话中引入了许多现实世界的复杂性。这些复杂性体现在上下文交互的复杂性增加、涵盖各种主题的延长对话会话以及更频繁的上下文转换形式。为了处理由不断发展的基于LLM的聊天系统引起的这些复杂性，我们提出了在开放域对话系统中每个片段的联合对话分割和状态跟踪。假设零样本设置适用于真正的开放域对话系统，我们提出了S3-DST，这是一种结构化提示技术，利用我们设计的用于改善长上下文跟踪的新型基础机制Pre-Analytical Recollection。为了展示我们提出的联合分割和状态跟踪方法的有效性，我们在专有的匿名开放域对话数据集以及公开可用的DST和分割数据集上评估了S3-DST。在所有数据集和设置中，S3-DST始终优于最先进技术，展示了其在下一代基于LLM的聊天系统中的效力和稳健性。

English

The traditional Dialogue State Tracking (DST) problem aims to track user preferences and intents in user-agent conversations. While sufficient for task-oriented dialogue systems supporting narrow domain applications, the advent of Large Language Model (LLM)-based chat systems has introduced many real-world intricacies in open-domain dialogues. These intricacies manifest in the form of increased complexity in contextual interactions, extended dialogue sessions encompassing a diverse array of topics, and more frequent contextual shifts. To handle these intricacies arising from evolving LLM-based chat systems, we propose joint dialogue segmentation and state tracking per segment in open-domain dialogue systems. Assuming a zero-shot setting appropriate to a true open-domain dialogue system, we propose S3-DST, a structured prompting technique that harnesses Pre-Analytical Recollection, a novel grounding mechanism we designed for improving long context tracking. To demonstrate the efficacy of our proposed approach in joint segmentation and state tracking, we evaluate S3-DST on a proprietary anonymized open-domain dialogue dataset, as well as publicly available DST and segmentation datasets. Across all datasets and settings, S3-DST consistently outperforms the state-of-the-art, demonstrating its potency and robustness the next generation of LLM-based chat systems.

S3-DST：LLM 时代的结构化开放域对话分割与状态跟踪

S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs

摘要

Summary

Support

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