# Adaptive Role-Play Prompting: A Reasoning Enhancement Technique for LLMs to Autonomously Select Roles

> This project proposes an innovative adaptive role-play prompting method that allows large language models (LLMs) to autonomously select the most suitable role identity based on the task, significantly improving strategic reasoning capabilities in zero-shot reasoning benchmark tests.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-29T21:51:50.000Z
- 最近活动: 2026-04-30T01:51:32.984Z
- 热度: 147.0
- 关键词: 大语言模型, 提示工程, 角色扮演, 零样本学习, 推理增强, 战略推理, 自适应方法, GitHub开源
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-dedmu5-adaptive-role-play-prompting
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-dedmu5-adaptive-role-play-prompting
- Markdown 来源: floors_fallback

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## Introduction: Adaptive Role-Play Prompting – A Reasoning Enhancement Technique for LLMs to Autonomously Select Roles

The dedmu5 team proposes an innovative adaptive role-play prompting method that allows large language models to autonomously select the most suitable role identity based on the task, significantly improving strategic reasoning capabilities in zero-shot reasoning benchmark tests. This project is open-source; the GitHub repository address is https://github.com/dedmu5/adaptive-role-play-prompting.

## Background: Evolution of Prompt Engineering and Limitations of Traditional Role-Play

As a key technique to unlock the capabilities of large language models, prompt engineering has evolved from simple instructions to complex templates. Role-play prompting improves output quality by having the model assume roles like experts, but traditional methods rely on manual experience to select roles, which is time-consuming and not necessarily optimal.

## Methodology: Core Mechanism and Implementation Framework of Adaptive Role-Play

The adaptive mechanism introduces a metacognitive layer, enabling the model to complete four steps: task analysis, role matching, role performance, and self-correction. The technology uses a two-stage architecture: the role selector chooses roles based on task domain, complexity, and output format; the role executor generates responses in the selected role. The role library covers multiple types such as analytical, creative, and technical roles.

## Evidence: Experimental Results and Performance Analysis

In zero-shot tests, compared to baseline methods: GSM8K math reasoning accuracy increased by 12-18%, HumanEval code generation pass rate increased by 8-15%, StrategyQA strategic question-answering F1 score increased by 10-14%, and BIG-Bench comprehensive reasoning average increased by 11%. Ablation experiments found that role diversity, fine-grained roles, dynamic switching, and domain-specific role libraries all improve performance.

## Application Value: Practical Scenarios and Advantages of the Adaptive Method

1. Reduces the threshold for prompt engineering; non-professional users do not need to design role prompts. 2. Enhances the capabilities of general assistants to flexibly meet diverse needs. 3. Provides new ideas for multi-agent collaboration; dynamically selecting roles for subtasks improves collaboration efficiency.

## Limitations and Future: Current Challenges and Research Directions

Current limitations: Dependence on predefined role libraries, increased overhead from two-stage reasoning, and limited effectiveness in highly specialized domains. Future directions: Dynamic role generation, multi-role collaboration, role memory mechanisms, and visual-language fusion expansion.

## Community Response and Related Resources

The project has gained attention since being open-sourced; developers praise its ease of use, reproducibility of results, and extensibility. The community has contributed domain-specific role libraries. Related resources: GitHub repository https://github.com/dedmu5/adaptive-role-play-prompting, paper preprint to be released soon, and example Notebooks containing multi-domain cases.