mlsanityData to 7 Oct 2026

CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions

arXiv 2405.00523 ↗First posted 1 May 2024cs.AI cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 65 references that are in the map. First posted on arXiv 1 May 2024.

Builds on

32 of 65 references.

References · 32

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20 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Culinary Class Wars: Evaluating LLMs using ASH in Cuisine Transfer Taskcited by 0
  2. 2026FlavourBench: Executable Culinary Reward Maps for Language Model Evaluation and Post-Trainingcited by 0

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  3. 2026Evaluation of LLMs in retrieving food and nutritional context for RAG systems
  4. 2024PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes
  5. 2026VArify: A Visual Analytics System for Verifying Knowledge Enhanced Large Language Model Responses in Food Science
  6. 2024FoodMLLM-JP: Leveraging Multimodal Large Language Models for Japanese Recipe Generation
  7. 2025Introducing the Swiss Food Knowledge Graph: AI for Context-Aware Nutrition Recommendation
  8. 2026CGU-ILALab at FoodBench-QA 2026: Comparing Traditional and LLM-based Approaches for Recipe Nutrient Estimation