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.

翻译：本研究探讨了大型语言模型在膳食规划中的应用效果，重点关注其识别与分解复合食材成分的能力。我们评估了三种模型——GPT-4o、Llama-3（70b）和Mixtral（8x7b）——以检验其在识别和解析复杂食材组合方面的性能。初步结果表明，虽然Llama-3（70b）和GPT-4o在精确分解方面表现突出，但所有模型在识别调味品、食用油等关键成分时均存在困难。尽管整体性能较强，各模型在准确性和完整性方面仍存在差异。这些发现凸显了大型语言模型在提升个性化营养管理方面的潜力，同时表明食材分解能力仍需进一步完善。未来研究应针对这些局限性进行改进，以提升营养建议的精准度与健康干预效果。