Summary of Recent Trends in Personalized Dialogue Generation: a Review Of Datasets, Methodologies, and Evaluations, by Yi-pei Chen et al.
Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations
by Yi-Pei Chen, Noriki Nishida, Hideki Nakayama, Yuji Matsumoto
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper systematically surveys the landscape of personalized dialogue generation in conversational agents, covering 22 datasets and 17 seminal works from top conferences between 2021-2023. The authors highlight benchmark datasets and newer ones enriched with additional features, identify five distinct types of problems, and shed light on recent progress by large language models (LLMs) in this area. They also provide a comprehensive summary of evaluation facets and metrics utilized in these works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make conversational agents more personal and engaging for users. It talks about different ways to do this, from making the agent sound like a specific person to understanding what users want to say or mean. The authors look at 22 datasets and 17 important papers that have been published recently, and they identify five main types of problems in this area. They also discuss how large language models are helping to make dialogue generation more personalized. |
