Task-oriented dialogue (TOD) systems enable users to achieve their goals through natural language interactions. Traditionally, these systems have relied on turn-level manually annotated metadata, such as dialogue states and policy annotations, which are expensive, time-consuming, and often inconsistent or error-prone. This dependence limits the potential to leverage vast amounts of readily available conversational data for training TOD systems. Additionally, a critical challenge in TOD system design is determining when and how to access and integrate information from external sources. Current approaches typically expect this information to be provided alongside the dialogue context, rather than learning to identify and retrieve it autonomously. While pre-trained large language models (LLMs) have been used to develop TOD systems, their potential to train such systems without laborious annotations remains largely unexplored. This work employs multi-task instruction fine-tuning to create more efficient and scalable TOD systems that can effectively leverage natural language conversational data without manual annotations, while autonomously managing external information retrieval. Our extensive experimental evaluations, using three diverse TOD datasets and three LLMs of varying sizes, demonstrate that our approach can generalize to new, unseen domains. Notably, our approach outperforms both state-of-the-art models trained on annotated data and billion-scale parameter off-the-shelf ChatGPT models.

翻译：任务导向对话系统使用户能够通过自然语言交互实现目标。传统上，这些系统依赖于轮级人工标注的元数据，如对话状态与策略标注，这类标注成本高昂、耗时且常存在不一致或易错问题。这种依赖性限制了利用海量易得的对话数据训练任务导向对话系统的潜力。此外，任务导向对话系统设计中的一个关键挑战在于如何确定何时以及如何从外部源获取并整合信息。现有方法通常期望此类信息随对话上下文一并提供，而非学习自主识别与检索信息。尽管预训练大语言模型已被用于开发任务导向对话系统，但其在不依赖繁重标注条件下训练此类系统的潜力仍很大程度上未被探索。本研究采用多任务指令微调方法，构建更高效、可扩展的任务导向对话系统，该系统能够有效利用无需人工标注的自然语言对话数据，同时自主管理外部信息检索。我们使用三个不同的任务导向对话数据集和三种不同规模的大语言模型进行了广泛的实验评估，结果表明我们的方法能够泛化至全新的未见领域。值得注意的是，我们的方法在性能上超越了基于标注数据训练的最先进模型以及具有数十亿参数规模的现成ChatGPT模型。