Problem
Research questions and friction points this paper is trying to address.
Large Language Models
Recipes Analysis
Diabetes
Dietary Guidelines
Investigating the Ability of Large Language Models to Analyze Recipes for Diabetes
๐ค AI Summary
็ ็ฉถๅฉ็จๅคงๅ่ฏญ่จๆจกๅๅๆ็ณๅฐฟ็
้็จ้ฃ่ฐฑ็่ฝๅ๏ผ้่ฟไธ็งๆ็คบๆนๆณๅไธไธชๅ
ๅซ7607ไธช้ฃ่ฐฑ็ๆฐๆฎ้ๆฅ่งฃๅณ่ฏๅซ้ๅ็ณๅฐฟ็
ๆฃ่
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๐ Abstract
Several studies have evaluated the ability of Large Language Models (LLMs) for meal planning, yielding positive outcomes. These models can process natural language inputs and leverage learned knowledge from their pretraining to generate meal plans. In this work, we investigate the ability of LLMs to analyze the suitability of given recipes for diabetes. The primary challenge for LLMs is to retrieve relevant dietary guidelines for diabetes, decompose recipes into ingredients and cooking methods, and apply these guidelines to determine the recipe's suitability. To study these challenges, we employ three kinds of prompts namely, (i) Direct Query Prompt (ii) Context-Guided Prompt, and (iii) Exemplary Context Prompt that incorporate different levels of diabetes dietary guidelines from medical sources. We introduce a benchmark dataset curated for this investigation consisting of 7607 recipes that include 3807 recipes suitable for diabetes and 3800 recipes not suitable for diabetes. Our results demonstrate that most LLMs are cautious in predicting recipes as suitable to prevent detrimental outcomes. Further, the models that can reason using the dietary guidelines performed better in predicting the suitability of recipes for diabetes. Overall, Mistral-7B and Llama 70B showed superior performance to their counterparts.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Large Language Models
Diabetes Dietary Guidelines
Recipe Analysis
Prompt Engineering
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