用大模型拆解餐单中的复合食材，提升个性化营养建议
Identifying and Decomposing Compound Ingredients in Meal Plans Using Large Language Models
- 用GPT-4o、Llama-3(70b)等大模型识别并分解复杂食材组合
- Llama-3(70b)和GPT-4o在分解准确率上表现优异，但对调味品识别差
- 适合关注智能营养规划与饮食分析的研究者和开发者
本研究探讨大语言模型在餐单规划中的有效性，重点评估其识别与分解复合食材的能力。我们测试了GPT-4o、Llama-3（70b）和Mixtral（8x7b）三款模型在复杂食材组合识别与拆解上的表现。初步结果显示，尽管Llama-3（70b）和GPT-4o在分解准确性上表现良好，但所有模型均难以识别盐、油等关键调味成分。尽管整体性能较强，不同模型在准确性和完整性上仍存在差异。这些发现表明大语言模型在提升个性化营养建议方面具有潜力，但仍需优化食材分解能力。未来研究应针对此局限性改进，以增强营养推荐与健康效果。
原文摘要 · Abstract (English)
This study explores the effectiveness of Large Language Models in meal planning, focusing on their ability to identify and decompose compound ingredients. We evaluated three models-GPT-4o, Llama-3 (70b), and Mixtral (8x7b)-to assess their proficiency in recognizing and breaking down complex ingredient combinations. Preliminary results indicate that while Llama-3 (70b) and GPT-4o excels in accurate decomposition, all models encounter difficulties with identifying essential elements like seasonings and oils. Despite strong overall performance, variations in accuracy and completeness were observed across models. These findings underscore LLMs' potential to enhance personalized nutrition but highlight the need for further refinement in ingredient decomposition. Future research should address these limitations to improve nutritional recommendations and health outcomes.
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