Summary of Cookingsense: a Culinary Knowledgebase with Multidisciplinary Assertions, by Donghee Choi et al.
CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions
by Donghee Choi, Mogan Gim, Donghyeon Park, Mujeen Sung, Hyunjae Kim, Jaewoo Kang, Jihun Choi
First submitted to arxiv on: 1 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces CookingSense, a descriptive knowledge base extracted from various sources including web data, scientific papers, and recipes. The collection is constructed using dictionary-based filtering and language model-based semantic filtering techniques. Additionally, the authors present FoodBench, a novel benchmark to evaluate culinary decision support systems. The authors empirically prove that CookingSense improves the performance of retrieval-augmented language models through evaluations with FoodBench. They also validate the quality and variety of assertions in CookingSense through qualitative analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a big database about cooking called CookingSense. It gets information from many different places like websites, science papers, and recipes. The authors use special techniques to make sure it’s accurate and organized. They also created a new way to test how well computers can help with cooking decisions called FoodBench. The results show that CookingSense helps computers do a better job of helping with cooking. The database is really useful and has lots of different types of information about food. |
Keywords
» Artificial intelligence » Knowledge base » Language model
