Summary of Llava-chef: a Multi-modal Generative Model For Food Recipes, by Fnu Mohbat and Mohammed J. Zaki
LLaVA-Chef: A Multi-modal Generative Model for Food Recipes
by Fnu Mohbat, Mohammed J. Zaki
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in large language models (LLMs) like GPT-2 and LLaVA have enabled Natural Language Processing (NLP) approaches for various food-related tasks. Despite their impressive performance, domain-specific training is crucial for effective application. This paper evaluates existing LLMs for recipe generation and proposes LLaVA-Chef, a novel model trained on a curated dataset of diverse recipe prompts using a multi-stage approach. The proposed model refines visual food image embeddings to the language space, fine-tunes it on relevant recipe data, utilizes diverse prompts to enhance recipe comprehension, and improves linguistic quality by penalizing the model with a custom loss function. LLaVA-Chef demonstrates impressive improvements over pretrained LLMs and prior works, generating more detailed recipes with precise ingredient mentions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machines better at understanding and creating food recipes. Researchers are using special computer models called large language models to help with this task. They’re trying to make these models work better by training them on lots of different recipe examples. The goal is to create a model that can generate detailed and accurate recipes, including ingredient lists. This new model is called LLaVA-Chef, and it does a great job of creating recipes that are more detailed and accurate than previous models. |
Keywords
» Artificial intelligence » Gpt » Loss function » Natural language processing » Nlp
