Summary of Crafting a Good Prompt or Providing Exemplary Dialogues? a Study Of In-context Learning For Persona-based Dialogue Generation, by Jiashu Pu et al.
Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation
by Jiashu Pu, Yajing Wan, Yuru Zhang, Jing Chen, Ling Cheng, Qian Shao, Yongzhu Chang, Tangjie Lv, Rongsheng Zhang
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 investigates the capabilities of large language models (LLMs) in persona-based dialogue generation using real human Chinese dialogue datasets. The study fills a gap in previous research on in-context learning (ICL) and its applications to dialogue generation tasks. The authors conduct extensive experiments, concluding that adjusting prompt instructions is the most effective way to improve generation quality. They also find that randomly retrieving demonstrations (demos) achieves better results than using demos with identical contexts. Surprisingly, even when the demos are corrupted, increasing their number still improves dialogue performance. This phenomenon cannot be fully explained by previous theories of ICL mechanisms like n-gram induction heads. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well large language models can create human-like conversations in different personas. The researchers test these models using real Chinese conversations and find that giving them better instructions is the best way to make their dialogues more natural. They also discover that using random examples of conversations helps, even if they’re not perfect matches for what’s being asked. This means that language models can learn from imperfect or incomplete information. |
Keywords
* Artificial intelligence * Prompt
