Going vegan with ChatGPT: Towards designing LLMs for personalized lifestyle changes
Bibliographic record
Abstract
Large language models (LLMs), one of the recent technological revolutions, have become applicable to all areas of human endeavor, including health. In the area of health, LLMs have contributed to disease management, diagnosis, stress management, and other major lifestyle-related changes. However, little is yet known about their impact in the area of nutrition and lifestyle-related changes associated with diseases such as diabetes, cardiovascular diseases, obesity, and others. In this paper, we present two case studies of ChatGPT as an LLM intervention for making lifestyle-related decisions, such as transitioning to a vegan lifestyle: 1. normal weight (healthy) and 2. obesity. Additionally, we considered three (3) dietary restrictions that could affect people in both case studies to transition to a vegan lifestyle. These include 1) allergies to nuts; 2) allergies to gluten; and 3) no allergies. We used ChatGPT to generate a one-week (seven-day) meal plan based on these dietary restrictions. We analyzed all responses from ChatGPT and found that ChatGPT provides a rich combination of vegan diets and is sensitive to these food allergies to some extent. Additionally, we found some challenges that relate to how an appropriate prompt can be employed to optimize ChatGPT’s recommendations and precisions relating to the total calories of foods recommended by ChatGPT. Furthermore, we provide recommendations to overcome these challenges in future work, including supporting user's domain-specific literacy and precision sensitivity for metrics that have an overall impact on human health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".