Summary of Multi-party Supervised Fine-tuning Of Language Models For Multi-party Dialogue Generation, by Xiaoyu Wang et al.
Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation
by Xiaoyu Wang, Ningyuan Xi, Teng Chen, Qingqing Gu, Yue Zhao, Xiaokai Chen, Zhonglin Jiang, Yong Chen, Luo Ji
First submitted to arxiv on: 6 Dec 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 This paper presents a novel approach to fine-tuning Large Language Models (LLMs) for participation in multi-party dialogues (MPD). Unlike previous research, which focused on the multi-agent framework but still used pairwisely fine-tuned LLMs, this work designs a multi-party fine-tuning framework (MuPaS) specifically for MPD datasets. The authors demonstrate that MuPaS can efficiently and effectively align LLMs with the multi-party conversation style, achieving state-of-the-art performance in MPD response generation, next-speaker prediction accuracy, and utterance quality evaluation. The framework also enables the generation of reasonable responses to out-of-distribution scene, topic, and role descriptions. This breakthrough has significant implications for applications such as conversation generation, virtual rehearsal, or meta-universe. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better at having conversations with more than two people. Right now, these models are great at talking one-on-one, but they struggle when there are multiple people involved. The researchers created a new way to train the models for multi-party conversations, which lets them understand and respond to these situations much better. They tested their approach and found that it produces much more realistic and coherent responses than previous methods. This could lead to all sorts of exciting applications, like generating conversation scripts or even creating virtual reality environments where people can have simulated conversations. |
Keywords
» Artificial intelligence » Fine tuning
