LLMs可加速可持续食品研发，结合优化算法减排79%。
What can large language models do for sustainable food?
- 用专家协作定义任务类型，评估六种LLM在四类任务中的表现。
- 在蛋白设计中，LLM比人类专家节省45%时间；菜单设计中需优化才能兼顾口感与碳排放。
- 提出新框架融合LLM与组合优化，实测餐厅菜单减排79%且满意度不降。
食物系统贡献了人类活动产生的三分之一温室气体排放。本文研究大型语言模型（LLMs）在降低食物生产环境影响方面的潜力。基于可持续食物领域的文献和领域专家合作，我们构建了一套设计与预测任务分类体系，并评估了六种LLM在四项任务中的表现。例如，在可持续蛋白设计任务中，食品科学专家评估显示，与LLM协作可平均减少45%的工作时间，优于另一名人类专家协作的22%。然而，在可持续菜单设计任务中，当指令要求同时考虑人类满意度与气候影响时，LLM生成的方案表现不佳。为此，我们提出一种通用框架，将LLM与组合优化结合以提升推理能力。在模拟餐厅场景中，该方法使食物选择的碳排放降低79%，同时保持参与者对选择组合的满意度。结果表明，结合优化技术后，LLMs在加速可持续食品开发与推广方面具有显著潜力。
原文摘要 · Abstract (English)
Food systems are responsible for a third of human-caused greenhouse gas emissions. We investigate what Large Language Models (LLMs) can contribute to reducing the environmental impacts of food production. We define a typology of design and prediction tasks based on the sustainable food literature and collaboration with domain experts, and evaluate six LLMs on four tasks in our typology. For example, for a sustainable protein design task, food science experts estimated that collaboration with an LLM can reduce time spent by 45% on average, compared to 22% for collaboration with another expert human food scientist. However, for a sustainable menu design task, LLMs produce suboptimal solutions when instructed to consider both human satisfaction and climate impacts. We propose a general framework for integrating LLMs with combinatorial optimization to improve reasoning capabilities. Our approach decreases emissions of food choices by 79% in a hypothetical restaurant while maintaining participants' satisfaction with their set of choices. Our results demonstrate LLMs' potential, supported by optimization techniques, to accelerate sustainable food development and adoption.
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