Identifying and Decomposing Compound Ingredients in Meal Plans Using Large Language Models

• URL: http://arxiv.org/abs/2411.05892v1
• Date: Fri, 08 Nov 2024 12:38:10 GMT
• Title: Identifying and Decomposing Compound Ingredients in Meal Plans Using Large Language Models
• Abstract summary: This study explores the effectiveness of Large Language Models in meal planning, focusing on their ability to identify and decompose compound ingredients. We evaluated three models-GPT-4o, Llama-3 (70b), and Mixtral (8x7b)-to assess their proficiency in recognizing and breaking down complex ingredient combinations. Preliminary results indicate that while Llama-3 (70b) and GPT-4o excels in accurate decomposition, all models encounter difficulties with identifying essential elements like seasonings and oils.
• Score: 6.477074442920329
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study explores the effectiveness of Large Language Models in meal planning, focusing on their ability to identify and decompose compound ingredients. We evaluated three models-GPT-4o, Llama-3 (70b), and Mixtral (8x7b)-to assess their proficiency in recognizing and breaking down complex ingredient combinations. Preliminary results indicate that while Llama-3 (70b) and GPT-4o excels in accurate decomposition, all models encounter difficulties with identifying essential elements like seasonings and oils. Despite strong overall performance, variations in accuracy and completeness were observed across models. These findings underscore LLMs' potential to enhance personalized nutrition but highlight the need for further refinement in ingredient decomposition. Future research should address these limitations to improve nutritional recommendations and health outcomes.

Related papers

• Investigating the Ability of Large Language Models to Analyze Recipes for Diabetes [0.9855443115851177]
  We introduce a benchmark dataset curated for this investigation consisting of 7607 recipes that include 3807 recipes suitable for diabetes and 3800 recipes not suitable for diabetes.<n>Our results demonstrate that most LLMs are cautious in predicting recipes as suitable to prevent detrimental outcomes.<n>Overall, Mistral-7B and Llama 70B showed superior performance to their counterparts.
  arXiv  Detail & Related papers  (2026-09-03T15:03:36Z)
• NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis [4.334404218740802]
  We show that existing multimodal large language models (MLLMs) are unreliable for comprehensive micronutrient estimation.<n>To address this gap without costly expert annotation, we repurposed a decade of 24-hour dietary recalls as structured prompts for text-to-image generation.<n>This pipeline produced a synthetic corpus of about 1.1 million image-description-nutrient triplets, each pairing a generated food image with a complete 65-nutrient label.
  arXiv  Detail & Related papers  (2026-06-08T02:50:08Z)
• GLEN-Bench: A Graph-Language based Benchmark for Nutritional Health [48.94971812317643]
  We introduce GLEN-Bench, the first comprehensive graph-language based benchmark for nutritional health assessment.<n>GLEN-Bench includes three linked tasks: risk detection identifies at-risk individuals from dietary and socioeconomic patterns; recommendation suggests personalized foods that meet clinical needs within resource constraints.<n>Our analysis identifies clear dietary patterns linked to health risks, providing insights that can guide practical interventions.
  arXiv  Detail & Related papers  (2026-01-26T03:32:46Z)
• Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion [69.84988999191343]
  We introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations.<n>We propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF$2$) method to enhance nutrition estimation.
  arXiv  Detail & Related papers  (2025-05-13T17:01:21Z)
• FlavorDiffusion: Predicting Food Pairings and Chemical Interactions Using Diffusion Models [0.0]
  This paper presents FlavorDiffusion, a novel framework leveraging diffusion models to predict food-chemical interactions and ingredient pairings. By integrating graph-based embeddings, diffusion processes, and chemical property encoding, FlavorDiffusion addresses data imbalances and enhances clustering quality. The proposed framework represents a significant step forward in computational gastronomy, offering scalable, interpretable, and chemically informed solutions for food science.
  arXiv  Detail & Related papers  (2025-02-08T06:47:27Z)
• MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation [50.309987904297415]
  Major food recommendation platforms such as Yelp prioritize users' dietary preferences over the healthiness of their choices. We develop a novel framework, Multi-Objective Personalized Interpretable Health-aware Food Recommendation System (MOPI-HFRS) It provides food recommendations by jointly optimizing the three objectives: user preference, personalized healthiness and nutritional diversity, along with a large language model (LLM)-enhanced reasoning module.
  arXiv  Detail & Related papers  (2024-12-12T01:02:09Z)
• Retrieval Augmented Recipe Generation [96.43285670458803]
  We propose a retrieval augmented large multimodal model for recipe generation. It retrieves recipes semantically related to the image from an existing datastore as a supplement. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation.
  arXiv  Detail & Related papers  (2024-11-13T15:58:50Z)
• FoodMLLM-JP: Leveraging Multimodal Large Language Models for Japanese Recipe Generation [30.349846688239293]
  We fine-tuned open MLLMs LLaVA-1.5 and Phi-3 Vision on a Japanese recipe dataset and benchmarked their performance against the closed model GPT-4o. Our evaluation demonstrates that the open models trained on recipe data outperform GPT-4o, the current state-of-the-art model, in ingredient generation.
  arXiv  Detail & Related papers  (2024-09-27T05:43:22Z)
• Optimizing Ingredient Substitution Using Large Language Models to Enhance Phytochemical Content in Recipes [0.0]
  This study explores how large language models (LLMs) can be applied to optimize ingredient substitutions in recipes. We fine-tuned models, including OpenAI's GPT-3.5, DaVinci, and Meta's TinyLlama, using an ingredient substitution dataset. These substitutions led to the creation of 1,951 phytochemically enriched ingredient pairings and 1,639 unique recipes.
  arXiv  Detail & Related papers  (2024-09-13T12:55:45Z)
• Towards Effective and Efficient Continual Pre-training of Large Language Models [163.34610964970258]
  Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. This paper presents a technical report for continually pre-training Llama-3 (8B) It significantly enhances the Chinese language ability and scientific reasoning ability of the backbone model.
  arXiv  Detail & Related papers  (2024-07-26T13:55:21Z)
• RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models [96.43285670458803]
  Uni-Food is a unified food dataset that comprises over 100,000 images with various food labels. Uni-Food is designed to provide a more holistic approach to food data analysis. We introduce a novel Linear Rectification Mixture of Diverse Experts (RoDE) approach to address the inherent challenges of food-related multitasking.
  arXiv  Detail & Related papers  (2024-07-17T16:49:34Z)
• NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images [63.314702537010355]
  Self-reporting methods are often inaccurate and suffer from substantial bias. Recent work has explored using computer vision prediction systems to predict nutritional information from food images. This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures.
  arXiv  Detail & Related papers  (2024-05-13T14:56:55Z)
• Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis [17.333822848423708]
  This study explores the application of multimodal ChatGPT within the realm of dietary assessment. By guiding the model with specific language prompts, GPT-4V shifts from recognizing common staples like rice and bread to accurately identifying regional dishes like banku and ugali. GPT-4V can leverage surrounding objects as scale references to deduce the portion sizes of food items, further enhancing its accuracy in translating food weight into nutritional content.
  arXiv  Detail & Related papers  (2023-12-14T01:26:45Z)
• Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models [51.57843608615827]
  The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry. We introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations.
  arXiv  Detail & Related papers  (2023-12-07T03:25:49Z)
• UMDFood: Vision-language models boost food composition compilation [26.5694236976957]
  We propose a novel vision-language model, UMDFood-VL, using front-of-package labeling and product images to accurately estimate food composition profiles. Up to 82.2% of selected products' estimated error between chemical analysis results and model estimation results are less than 10%. This performance sheds light on generalization towards other food and nutrition-related data compilation and catalyzation.
  arXiv  Detail & Related papers  (2023-05-18T03:18:12Z)
• FoodChem: A food-chemical relation extraction model [0.0]
  We present a new Relation Extraction (RE) model for identifying chemicals present in the composition of food entities. The BioBERT model achieves the best results, with a macro averaged F1 score of 0.902 in the unbalanced augmentation setting.
  arXiv  Detail & Related papers  (2021-10-05T13:07:33Z)
• Vision-Based Food Analysis for Automatic Dietary Assessment [49.32348549508578]
  This review presents one unified Vision-Based Dietary Assessment (VBDA) framework, which generally consists of three stages: food image analysis, volume estimation and nutrient derivation. Deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition.
  arXiv  Detail & Related papers  (2021-08-06T05:46:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.