This paper introduces CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes, from which knowledge covering a broad range of aspects is acquired. CookingSense is constructed through a series of dictionary-based filtering and language model-based semantic filtering techniques, which results in a rich knowledgebase of multidisciplinary food-related assertions. Additionally, we present FoodBench, a novel benchmark to evaluate culinary decision support systems. From evaluations with FoodBench, we empirically prove that CookingSense improves the performance of retrieval augmented language models. We also validate the quality and variety of assertions in CookingSense through qualitative analysis.

翻译：本文提出了CookingSense，这是一个从网络数据、科学论文及食谱等多种来源中提取的烹饪领域知识断言描述性集合，由此获取了涵盖广泛方面的知识。CookingSense通过一系列基于词典的过滤和基于语言模型的语义过滤技术构建而成，最终形成一个包含多学科食品相关断言的丰富知识库。此外，我们提出了FoodBench，一个用于评估烹饪决策支持系统的新型基准测试。通过FoodBench的评估，我们实证证明了CookingSense能提升检索增强语言模型的性能。我们还通过定性分析验证了CookingSense中断言的质量与多样性。