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.

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.

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.

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.

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.

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.

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.