让大模型扮演用户，生成更符合个人偏好的产品摘要。
Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models
- 让大模型模拟用户角色，理解个性化需求。
- 通过虚拟用户反馈干预，提升摘要相关性。
- 无需训练数据，适合个性化内容生成场景。
个性化意见摘要对满足用户兴趣至关重要。尽管大语言模型在无需训练数据的情况下具备强大的文本摘要与评估能力，但在处理涉及长文本的个性化任务时仍存在挑战。为此，本文提出基于大语言模型的角色扮演框架 Rehearsal。该框架让模型以用户身份进行角色扮演，从而更好地理解用户的个性化需求。同时引入角色扮演监督机制与练习流程，提升模型的角色表现能力，使用户需求表达更准确。此外，通过虚拟用户提出的建议对摘要生成过程进行干预，确保生成内容包含用户关注的信息，实现真正个性化的摘要生成。实验结果表明，该方法能有效提升大模型生成摘要的个性化水平。
原文摘要 · Abstract (English)
Personalized opinion summarization is crucial as it considers individual user interests while generating product summaries. Recent studies show that although large language models demonstrate powerful text summarization and evaluation capabilities without the need for training data, they face difficulties in personalized tasks involving long texts. To address this, \textbf{Rehearsal}, a personalized opinion summarization framework via LLMs-based role-playing is proposed. Having the model act as the user, the model can better understand the user's personalized needs. Additionally, a role-playing supervisor and practice process are introduced to improve the role-playing ability of the LLMs, leading to a better expression of user needs. Furthermore, through suggestions from virtual users, the summary generation is intervened, ensuring that the generated summary includes information of interest to the user, thus achieving personalized summary generation. Experiment results demonstrate that our method can effectively improve the level of personalization in large model-generated summaries.
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