与用户排练：基于大语言模型角色扮演的个性化观点摘要
Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models
AI总结：
针对大语言模型在长文本个性化摘要任务中的困难，提出基于角色扮演的Rehearsal框架，通过让模型扮演用户、引入监督器与练习过程及虚拟用户建议干预，有效提升了摘要的个性化水平。
摘要：
个性化观点摘要非常重要，因为它在生成产品摘要时会考虑个人用户的兴趣。近期研究表明，尽管大语言模型无需训练数据即展现出强大的文本摘要与评估能力，但在处理长文本的个性化任务时仍面临困难。为解决此问题，提出了Rehearsal，一个基于LLM角色扮演的个性化观点摘要框架。通过让模型扮演用户，模型能更好地理解用户的个性化需求。此外，引入了角色扮演监督器和练习过程以提升LLM的角色扮演能力，从而更好地表达用户需求。进一步地，通过虚拟用户的建议干预摘要生成，确保生成的摘要包含用户感兴趣的信息，从而实现个性化摘要生成。实验结果表明，我们的方法能有效提升大模型生成摘要的个性化水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
