个性化对话生成的近期趋势：数据集、方法论与评估综述
Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations
AI总结：
本文系统综述了个性化对话生成的近期研究，涵盖22个数据集、2021-2023年间17项顶级会议论文及LLMs进展，总结了方法论、评估指标与未来挑战。
摘要：
通过对话代理中的个性化来提升用户参与度已变得日益重要，尤其是在能够生成流畅回复的大语言模型出现之后。然而，个性化对话生成是多方面的，其定义各不相同——从向代理注入角色到捕捉用户的显式和隐式线索。本文旨在系统地调查个性化对话生成的近期研究现状，包括所使用的数据集、开发的方法论以及应用的评估指标。通过涵盖22个数据集，我们重点介绍了基准数据集以及融入了额外特征的新数据集。我们进一步分析了2021至2023年间顶级会议上的17项开创性工作，并识别出五种不同类型的问题。我们还探讨了LLMs在个性化对话生成方面的最新进展。我们的评估部分全面总结了这些作品中使用的评估方面和指标。最后，我们讨论了当前面临的挑战，并展望了个性化对话生成未来研究的潜在方向。
英文摘要：
Enhancing user engagement through personalization in conversational agents has gained significance, especially with the advent of large language models that generate fluent responses. Personalized dialogue generation, however, is multifaceted and varies in its definition -- ranging from instilling a persona in the agent to capturing users' explicit and implicit cues. This paper seeks to systemically survey the recent landscape of personalized dialogue generation, including the datasets employed, methodologies developed, and evaluation metrics applied. Covering 22 datasets, we highlight benchmark datasets and newer ones enriched with additional features. We further analyze 17 seminal works from top conferences between 2021-2023 and identify five distinct types of problems. We also shed light on recent progress by LLMs in personalized dialogue generation. Our evaluation section offers a comprehensive summary of assessment facets and metrics utilized in these works. In conclusion, we discuss prevailing challenges and envision prospect directions for future research in personalized dialogue generation.
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。
