Summary of Identifying and Decomposing Compound Ingredients in Meal Plans Using Large Language Models, by Leon Kopitar et al.
Identifying and Decomposing Compound Ingredients in Meal Plans Using Large Language Models
by Leon Kopitar, Leon Bedrac, Larissa J Strath, Jiang Bian, Gregor Stiglic
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 This study investigates the abilities of Large Language Models (LLMs) in meal planning, focusing on their capacity to identify and decompose complex ingredients. Three models – GPT-4o, Llama-3 (70b), and Mixtral (8x7b) – were evaluated for proficiency in recognizing and breaking down compound ingredient combinations. The results suggest that while Llama-3 (70b) and GPT-4o excel in accurate decomposition, all models struggle with identifying essential elements like seasonings and oils. Despite strong overall performance, variations in accuracy and completeness were observed across models. This study highlights the potential of LLMs to enhance personalized nutrition but emphasizes the need for further refinement in ingredient decomposition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well Large Language Models (LLMs) can help with meal planning by identifying and breaking down ingredients. The researchers tested three different models to see which one is best at doing this job. They found that two of the models, GPT-4o and Llama-3 (70b), are really good at breaking down ingredients, but all of them struggle to identify important things like seasonings and oils. Even though the models do well overall, they don’t always get it right, which is a problem if we want to use them to make healthy meal plans. This study shows that LLMs have potential to help with personalized nutrition, but more work needs to be done to make sure they’re accurate. |
Keywords
» Artificial intelligence » Gpt » Llama
