Data Augmentation to Improve Large Language Models in Food Hazard and Product Detection

2025-02-12 · Areeg Fahad Rasheed, M. Zarkoosh, Shimam Amer Chasib, Safa F. Abbas

The primary objective of this study is to demonstrate the impact of data augmentation using ChatGPT-4o-mini on food hazard and product analysis. The augmented data is generated using ChatGPT-4o-mini and subsequently used to train two large language models: RoBERTa-base and Flan-T5-base. The models are evaluated on test sets. The results indicate that using augmented data helped improve model performance across key metrics, including recall, F1 score, precision, and accuracy, compared to using only the provided dataset. The full code, including model training and the augmented dataset, can be found in this repository: https://github.com/AREEG94FAHAD/food-hazard-prdouct-cls

📄 PDF Abstract BibTeX arXiv:2502.08687

Code (1)

areeg94fahad/food-hazard-prdouct-cls 공식 구현

Tasks

Data Augmentation

BrightCookies at SemEval-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification

2025-04-29 · Foteini Papadopoulou, Osman Mutlu, Neris Özen, Bas H. M. van der Velden 외

This paper presents our system developed for the SemEval-2025 Task 9: The Food Hazard Detection Challenge. The shared task's objective is to evaluate explainable classification systems for classifying hazards and product…

Data Augmentation Text Augmentation

MALM: Mask Augmentation based Local Matching for Food-Recipe Retrieval

2023-05-18 · Bhanu Prakash Voutharoja, Peng Wang, Lei Wang, Vivienne Guan

Image-to-recipe retrieval is a challenging vision-to-language task of significant practical value. The main challenge of the task lies in the ultra-high redundancy in the long recipe and the large variation reflected in …

Image-text matching Retrieval Text Matching

LLMs-based Augmentation for Domain Adaptation in Long-tailed Food Datasets

2025-11-20 · Qing Wang, Chong-Wah Ngo, Ee-Peng Lim, Qianru Sun arxiv

Training a model for food recognition is challenging because the training samples, which are typically crawled from the Internet, are visually different from the pictures captured by users in the free-living environment.…

Domain Adaptation

Batch Augmentation with Unimodal Fine-tuning for Multimodal Learning

2025-05-10 · H M Dipu Kabir, Subrota Kumar Mondal, Mohammad Ali Moni

This paper proposes batch augmentation with unimodal fine-tuning to detect the fetus's organs from ultrasound images and associated clinical textual information. We also prescribe pre-training initial layers with investi…

Image Augmentation Large Language Model Multimodal Large Language Model

Thought-For-Food: Reasoning Chain Induced Food Visual Question Answering

2025-11-03 · Riddhi Jain, Manasi Patwardhan, Parijat Deshpande, Venkataramana Runkana arxiv

The immense diversity in the culture and culinary of Indian cuisines calls attention to the major shortcoming of the existing Visual Question Answering(VQA) systems which are inclined towards the foods from Western regio…

Visual Question Answering Reinforcement Learning