CookingSense：一个包含多学科断言的烹饪知识库
CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions
AI总结：
该研究构建了源自网络、论文和食谱的多学科烹饪知识库 CookingSense，并提出 FoodBench 基准，证明其可提升检索增强语言模型的烹饪决策支持性能。
摘要：
本文介绍了 CookingSense，这是一个描述性的烹饪领域知识断言集合，提取自包括网络数据、科学论文和食谱在内的多种来源，并从中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，形成了一个丰富的、包含多学科食品相关断言的知识库。此外，我们提出了 FoodBench，这是一个用于评估烹饪决策支持系统的新型基准。通过使用 FoodBench 进行评估，我们凭经验证明 CookingSense 提升了检索增强语言模型的性能。我们还通过定性分析验证了 CookingSense 中断言的质量和多样性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
