PA-CoT: Profile-Adaptive Chain-of-Thought for Personalized Nutritional Consulting
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Computer Science > Human-Computer Interaction
Title:PA-CoT: Profile-Adaptive Chain-of-Thought for Personalized Nutritional Consulting
Abstract:In health and nutrition consulting, widely used prompting methods pass the user profile as an unstructured block without a dedicated analysis step, leaving personalization as a critical structural gap. We introduce PA-CoT (Profile-Adaptive Chain-of-Thought), a multi-stage prompting method that treats profile interpretation as an explicit, standalone reasoning step prior to response generation. To enable systematic evaluation, we introduce the QPA (Question--Profile--Answer) benchmark -- 200 nutritional consulting samples with structured user profiles scored on four criteria. In a comparative study against 11 comparison methods (CoT, Few-Shot, Role Prompting, DSPy, TextGrad, Self-Refine, and others, plus a Zero-Shot Baseline; 12 total including PA-CoT), PA-CoT achieves the best average score (4.21 on the G-Eval 1--5 scale) and leads on both Personalization (4.71 vs. 4.39) and Safety (4.68 vs. 4.52) with non-overlapping 95\% confidence intervals over the nearest competitor -- the only method to simultaneously top both criteria. The results confirm that an explicit profile-analysis step is the key driver of personalization gains over widely used prompting approaches.
| Comments: | Accepted at the ICML 2026 Workshop on Structured Data for Health (SD4H) |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.24907 [cs.HC] |
| (or arXiv:2608.24907v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2608.24907
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