基于LLM的无监督端到端任务型对话：噪声通道的力量
Unsupervised End-to-End Task-Oriented Dialogue with LLMs: The Power of the Noisy Channel
AI总结：
提出基于期望最大化和噪声通道模型的无监督端到端任务型对话方法，仅利用API模式和无标签对话推断轮次级标注，在MultiWOZ上使GPT-3.5基线的对话成功率提升一倍以上。
摘要：
训练任务型对话系统通常需要与API交互的轮次级标注：例如每步的对话状态和系统动作。这些标注制作成本高、易出错，且需要领域与标注专业知识。随着LLM的进步，我们假设仅凭无标签数据和模式定义即可完全无监督地构建可用的任务型对话系统。我们考虑一种新颖的无监督设定，仅包含(1)定义良好的API模式(2)一组用户与代理间的无标签对话。我们提出一种创新方法，利用期望最大化（EM）通过噪声通道模型将轮次级标注推断为隐变量，以构建端到端对话代理。在MultiWOZ基准上评估，我们的方法使强GPT-3.5基线的对话成功率提升了一倍以上。
英文摘要：
Training task-oriented dialogue systems typically requires turn-level annotations for interacting with their APIs: e.g. a dialogue state and the system actions taken at each step. These annotations can be costly to produce, error-prone, and require both domain and annotation expertise. With advances in LLMs, we hypothesize that unlabeled data and a schema definition are sufficient for building a working task-oriented dialogue system, completely unsupervised. We consider a novel unsupervised setting of only (1) a well-defined API schema (2) a set of unlabeled dialogues between a user and agent. We propose an innovative approach using expectation-maximization (EM) that infers turn-level annotations as latent variables using a noisy channel model to build an end-to-end dialogue agent. Evaluating our approach on the MultiWOZ benchmark, our method more than doubles the dialogue success rate of a strong GPT-3.5 baseline.
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。
