探究大型语言模型分析糖尿病适用食谱的能力
Investigating the Ability of Large Language Models to Analyze Recipes for Diabetes
- AI Institute, University of South Carolina（南卡罗来纳大学人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究探究大型语言模型分析糖尿病适用食谱的能力，采用三类提示和含7607份食谱的基准数据集，发现Mistral-7B和Llama 70B表现更优。
AI中文摘要：
多项研究已评估大型语言模型（LLMs）在膳食规划方面的能力，取得了积极成果，这些模型可处理自然语言输入，并利用预训练所学知识生成膳食计划。本研究探究LLMs分析给定食谱对糖尿病患者适用性的能力，LLMs面临的主要挑战包括：检索相关的糖尿病饮食指南、将食谱分解为食材和烹饪方法，以及应用这些指南确定食谱的适用性。为研究这些挑战，我们采用三类提示：（i）直接查询提示（Direct Query Prompt）、（ii）上下文引导提示（Context-Guided Prompt）、（iii）示例上下文提示（Exemplary Context），这些提示整合了不同级别的医学来源糖尿病饮食指南。我们引入了为本次研究整理的基准数据集，包含7607份食谱，其中3807份为糖尿病适用食谱，3800份为非适用食谱。结果表明，大多数LLMs在预测食谱适用性时较为谨慎，以避免产生有害结果；此外，能够利用饮食指南进行推理的模型在预测糖尿病食谱适用性时表现更好。总体而言，Mistral-7B和Llama 70B的性能优于其他同类模型。
英文摘要：
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.
