用大模型当味觉科学家，自动提出风味配方新思路
FoodPuzzle: Developing Large Language Model Agents as Flavor Scientists
- 构建科学智能体，结合上下文学习与检索增强生成风味假设
- 在978种食物、1766种风味分子的FoodPuzzle基准上表现超越传统方法
- 适合食品研发、AI辅助创意设计的研究者和从业者
食品行业风味研发面临快速创新与精准配比的双重挑战。传统依赖反复主观测试的方法效率低、难扩展。本文提出三个贡献：定义风味科学中智能体生成假设的新任务；构建包含978种食物和1766种风味分子的FoodPuzzle基准；提出融合上下文学习与检索增强的科学智能体方法，生成基于证据的风味假设。实验表明，该模型在风味预测任务中显著优于传统方法，展现出变革风味开发实践的潜力。
原文摘要 · Abstract (English)
Flavor development in the food industry is increasingly challenged by the need for rapid innovation and precise flavor profile creation. Traditional flavor research methods typically rely on iterative, subjective testing, which lacks the efficiency and scalability required for modern demands. This paper presents three contributions to address the challenges. Firstly, we define a new problem domain for scientific agents in flavor science, conceptualized as the generation of hypotheses for flavor profile sourcing and understanding. To facilitate research in this area, we introduce the FoodPuzzle, a challenging benchmark consisting of 978 food items and 1,766 flavor molecules profiles. We propose a novel Scientific Agent approach, integrating in-context learning and retrieval augmented techniques to generate grounded hypotheses in the domain of food science. Experimental results indicate that our model significantly surpasses traditional methods in flavor profile prediction tasks, demonstrating its potential to transform flavor development practices.
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