# Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models

arXiv: https://arxiv.org/abs/2503.00449 · HTML: https://mlsanity.com/paper/2503.00449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 58 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 25 of 25)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023)
- [Self-collaboration Code Generation via ChatGPT](https://mlsanity.com/paper/2304.07590/) (2023)
- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks](https://mlsanity.com/paper/2305.18365/) (2023)
- [Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost](https://mlsanity.com/paper/2407.19825/) (2024)
- [Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate](https://mlsanity.com/paper/2305.11595/) (2023)
- [Guiding Large Language Models via Directional Stimulus Prompting](https://mlsanity.com/paper/2302.11520/) (2023)
- [FineSurE: Fine-grained Summarization Evaluation using LLMs](https://mlsanity.com/paper/2407.00908/) (2024)
- [Unsupervised Opinion Summarization as Copycat-Review Generation](https://mlsanity.com/paper/1911.02247/) (2019)
- [Are LLMs All You Need for Task-Oriented Dialogue?](https://mlsanity.com/paper/2304.06556/) (2023)
- [Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement](https://mlsanity.com/paper/2402.11060/) (2024)
- [Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion](https://mlsanity.com/paper/2311.06318/) (2023)
- [Unsupervised Opinion Summarization with Noising and Denoising](https://mlsanity.com/paper/2004.10150/) (2020)
- [Personalized LLM Response Generation with Parameterized Memory Injection](https://mlsanity.com/paper/2404.03565/) (2024)
- [Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation](https://mlsanity.com/paper/2305.14386/) (2023)
- [Convex Aggregation for Opinion Summarization](https://mlsanity.com/paper/2104.01371/) (2021)
- [One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation](https://mlsanity.com/paper/2402.11683/) (2024)
- [Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations](https://mlsanity.com/paper/2306.01475/) (2023)
- [Towards Personalized Review Summarization by Modeling Historical Reviews from Customer and Product Separately](https://mlsanity.com/paper/2301.11682/) (2023)
- [Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals](https://mlsanity.com/paper/2309.08949/) (2023)

## Built on by (most cited first, 2 of 2)

- [Modeling Motivated Reasoning in Law: Evaluating Strategic Role Conditioning in LLM Summarization](https://mlsanity.com/paper/2509.00529/) (2025)
- [CAP-LLM: Context-Augmented Personalized Large Language Models for News Headline Generation](https://mlsanity.com/paper/2508.03935/) (2025)

## Abstract (arXiv)

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, 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.