Summary
This paper investigates whether structured intent representation can generalize across languages and models. The authors study PPS (Prompt Protocol Specification), a structured intent representation framework for human-AI interaction based on the 5W3H framework. Analyzing 2,160 model outputs spanning 3 languages, 4 conditions, 3 large language models, and 60 tasks, the study finds that AI-expanded 5W3H prompts (Condition D) show no statistically significant difference in goal alignment compared to manually written 5W5H prompts (Condition C) across all three languages, while requiring users to provide only a single-sentence input. Structured PPS conditions generally reduce or reshape output variance across models. The paper is listed under machine learning on arXiv (2603.25379) and authored by Peng Gang, with findings relevant to prompt engineering and cross-lingual LLM evaluation.
Paper Overview
Field: Machine Learning
Author: Peng Gang
Published: 2026-03-26
arXiv: 2603.25379
Abstract
Can structured intent representation generalize across languages and models? This paper examines PPS (Prompt Protocol Specification), a structured intent representation framework for human-AI interaction based on 5W3H.
Across 2,160 model outputs (3 languages × 4 conditions × 3 LLMs × 60 tasks), the study finds that AI-expanded 5W3H prompts (Condition D) show no statistically significant difference in goal alignment compared to manually written 5W5H prompts (Condition C) across all three languages — while users only need to input a single sentence. Structured PPS conditions generally reduce or reshape output variance across models.
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