🤖 AI Summary
This study addresses the challenge of reliably maintaining user goal consistency across multiple models, languages, and prompting frameworks. The authors propose a protocol-like communication layer grounded in structured intent representations—specifically the 5W3H schema—and systematically evaluate its cross-lingual and cross-domain alignment efficacy on Claude, GPT-4o, and Gemini 2.5 Pro. Leveraging both automated evaluation with DeepSeek-V3 and a user study involving 50 participants, the results demonstrate that structured prompting substantially reduces cross-lingual goal drift, lowering the standard deviation of alignment scores from 0.470 to 0.020. The findings further reveal a weak-model compensation effect and the critical role of dimensional decomposition. Notably, Gemini exhibits a +1.006 improvement in goal alignment, accompanied by a 60% reduction in interaction turns and an increase in user satisfaction from 3.16 to 4.04.
📝 Abstract
How reliably can structured intent representations preserve user goals across different AI models, languages, and prompting frameworks? Prior work showed that PPS (Prompt Protocol Specification), a 5W3H-based structured intent framework, improves goal alignment in Chinese and generalizes to English and Japanese. This paper extends that line of inquiry in three directions: cross-model robustness across Claude, GPT-4o, and Gemini 2.5 Pro; controlled comparison with CO-STAR and RISEN; and a user study (N=50) of AI-assisted intent expansion in ecologically valid settings. Across 3,240 model outputs (3 languages x 6 conditions x 3 models x 3 domains x 20 tasks), evaluated by an independent judge (DeepSeek-V3), we find that structured prompting substantially reduces cross-language score variance relative to unstructured baselines. The strongest structured conditions reduce cross-language sigma from 0.470 to about 0.020. We also observe a weak-model compensation pattern: the lowest-baseline model (Gemini) shows a much larger D-A gain (+1.006) than the strongest model (Claude, +0.217). Under the current evaluation resolution, 5W3H, CO-STAR, and RISEN achieve similarly high goal-alignment scores, suggesting that dimensional decomposition itself is an important active ingredient. In the user study, AI-expanded 5W3H prompts reduce interaction rounds by 60 percent and increase user satisfaction from 3.16 to 4.04. These findings support the practical value of structured intent representation as a robust, protocol-like communication layer for human-AI interaction.