A Large-Scale Evaluation of Role-Enhanced Zero-Shot Chain-of-Thought Prompting
This paper proposes Role-Enhanced Zero-Shot Chain-of-Thought (RE-ZS-CoT), a prompting framework that systematically integrates domain-specific expert roles to enhance reasoning in large language models (LLMs) without task-specific examples.
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Key points
- We assess two versions of our approach.
- In the first version, static role selection, we evaluate eight predefined expert roles across arithmetic, commonsense, and reading comprehension benchmarks to determine their impact on reasoning performance.
- In the second version, dynamic role selection, each model autonomously selects the single most suitable role for each benchmark, enabling adaptive task-role alignment without manual specification.
- RE-ZS-CoT advances zero-shot prompting by unifying role specialization with chain-of-thought reasoning, offering a practical, resource-efficient solution for improving LLM capabilities across diverse domains.
Sources (1)
- [1]A Large-Scale Evaluation of Role-Enhanced Zero-Shot Chain-of-Thought PromptingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 01:53 AM
This paper proposes Role-Enhanced Zero-Shot Chain-of-Thought (RE-ZS-CoT), a prompting framework that systematically integrates domain-specific expert roles to enhance reasoning in large language models (LLMs) without task-specific examples.
We assess two versions of our approach.
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