🤖 AI Summary
This study investigates whether a structured intent representation based on the 5W3H framework can effectively generalize across multiple languages (Chinese, English, and Japanese) and diverse large language models to enhance intent alignment and accessibility. Leveraging the PPS framework, the authors conduct 2,160 cross-lingual and cross-model controlled experiments under four prompting conditions. They demonstrate for the first time that AI-generated 5W3H prompts perform comparably to human-crafted ones, significantly reducing user input burden. The work also uncovers a “dual inflation bias” in unstructured prompts, whose deceptively low output variance misrepresents model behavior. Results show that structured prompting not only improves target alignment but also reshapes and more accurately reflects cross-model output variance, offering a novel paradigm for prompt engineering.
📝 Abstract
Does structured intent representation generalize across languages and models? We study PPS (Prompt Protocol Specification), a 5W3H-based framework for structured intent representation in human-AI interaction, and extend prior Chinese-only evidence along three dimensions: two additional languages (English and Japanese), a fourth condition in which a user's simple prompt is automatically expanded into a full 5W3H specification by an AI-assisted authoring interface, and a new research question on cross-model output consistency. Across 2,160 model outputs (3 languages x 4 conditions x 3 LLMs x 60 tasks), we find that AI-expanded 5W3H prompts (Condition D) show no statistically significant difference in goal alignment from manually crafted 5W3H prompts (Condition C) across all three languages, while requiring only a single-sentence input from the user. Structured PPS conditions often reduce or reshape cross-model output variance, though this effect is not uniform across languages and metrics; the strongest evidence comes from identifying spurious low variance in unconstrained baselines. We also show that unstructured prompts exhibit a systematic dual-inflation bias: artificially high composite scores and artificially low apparent cross-model variance. These findings suggest that structured 5W3H representations can improve intent alignment and accessibility across languages and models, especially when AI-assisted authoring lowers the barrier for non-expert users.