基于大语言模型的忠实人格型对话数据集生成
Faithful Persona-based Conversational Dataset Generation with Large Language Models
AI总结：
本研究提出基于生成器-评判器架构的大语言模型框架，以Persona-Chat为种子生成含2万条对话的Synthetic-Persona-Chat人格型数据集，经三轮迭代后图灵测试落败率从17.2%降至8.8%。
摘要：
高质量对话数据集对于开发能与用户交流的AI模型至关重要。促进聊天机器人与用户之间更深层互动的一种方式是通过人格（persona）——即能体现用户性格、动机和行为的用户特征维度。在多样且全面的人格型数据集上训练自然语言处理（NLP）模型，可让对话模型与用户建立更深的联结并维持用户参与度。本文中，我们利用大语言模型（LLM）的能力，从种子数据集出发构建大规模高质量对话数据集。我们提出一种生成器-评判器（Generator-Critic）架构框架，在扩展初始数据集的同时提升对话质量。生成器是经提示输出对话的LLM。评判器由专家LLM混合组成，用于管控生成对话的质量，这些专家会选出最优生成对话，我们再用这些对话优化生成器。我们发布了Synthetic-Persona-Chat数据集，包含以Persona-Chat为种子生成的2万条对话。我们通过大量实验从不同维度评估了Synthetic-Persona-Chat及我们的生成框架的质量，结果显示，在图灵测试中，Synthetic-Persona-Chat相较于Persona-Chat的落败率在三轮迭代后从17.2%降至8.8%。
英文摘要：
High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user's character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement. In this paper, we leverage the power of Large Language Models (LLMs) to create a large, high-quality conversational dataset from a seed dataset. We propose a Generator-Critic architecture framework to expand the initial dataset, while improving the quality of its conversations. The Generator is an LLM prompted to output conversations. The Critic consists of a mixture of expert LLMs that control the quality of the generated conversations. These experts select the best generated conversations, which we then use to improve the Generator. We release Synthetic-Persona-Chat, consisting of 20k conversations seeded from Persona-Chat. We evaluate the quality of Synthetic-Persona-Chat and our generation framework on different dimensions through extensive experiments, and observe that the losing rate of Synthetic-Persona-Chat against Persona-Chat during Turing test decreases from 17.2% to 8.8% over three iterations.
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。
