When Does Persona Prompting Actually Help? A Retrieval and Metric Analysis of Expert Role Injection in LLMs
arXiv:2605.29420v1 Announce Type: cross Abstract: Persona prompting is widely used to steer large language models, yet its practical value remains unclear. Prior work often evaluates persona prompting using aggregate scores, making it difficult to determine whether expert-role prompting consistently improves response quality or instead changes responses along different quality dimensions. We study this question through a controlled comparison of four prompting conditions across 1,140 open-ended questions spanning 38 expert roles and six domains: no role prompt, a generic domain-expert prompt,
The proliferation of LLM applications makes understanding effective prompting techniques critical for achieving desired outcomes and optimizing performance.
This research provides a more granular understanding of persona prompting efficacy, moving beyond aggregate scores to pinpoint its real value and limitations in steering LLMs.
The understanding of persona prompting shifts from a broad assumption of benefit to a nuanced view, informing more precise and effective LLM application design.
- · AI researchers
- · LLM developers
- · Prompt engineering platforms
- · Ineffective prompt engineering practices
- · Over-reliance on generic persona prompts
More sophisticated and targeted prompt engineering strategies will emerge.
Improved efficiency and accuracy in LLM applications across various domains will be achieved.
The development of adaptive, context-aware prompting systems could accelerate, leading to more robust and versatile AI agents.
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Read at arXiv cs.LG