Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation
Authors: Jiashu Pu, Yajing Wan, Yuru Zhang, Jing Chen, ling Cheng, Qian Shao, Yongzhu Chang, Xianyang Qi, Tangjie Lv, Rongsheng Zhang


Reviewer paper summaries: The paper investigates the capabilities of LLMs in generating human-like persona-based dialogues through in-context learning. The study uses Chinese dialogue datasets to conduct extensive experiments and derive three main conclusions: adjusting prompt instructions effectively improves dialogue quality, random retrieval of dialogue examples yields the best results due to diversity, and even corrupted dialogue examples can enhance performance by increasing their number.
Summary of strengths: 1. The extensive experiments on Chinese dialogue datasets provide valuable insights into how different prompt adjustments and demo retrieval methods impact dialogue generation quality, offering practical guidelines for optimizing these processes.

2. The findings suggest that adjusting prompt instructions and using random demo retrievals can significantly improve dialogue quality without the high costs associated with fine-tuning, making it a cost-effective approach.
Summary of weaknesses: 1. The experiments are conducted solely on Chinese dialogue datasets. This limits the generalizability of the findings to other languages.

2. The evaluation relies heavily on human annotators who are also co-authors. This could introduce bias in the subjective assessments of response quality, potentially affecting the validity of the results.
Comments, suggestions and typos: The authors could consider including experiments with dialogue datasets in different languages or domains. This would enhance the generalizability of your findings.

The authors should consider incorporating a more diverse set of evaluators other than co-authors. This can help mitigate potential biases in the evaluation process.
Overall assessment scores: 2.5