# SemEval-2025 Task 9: The Food Hazard Detection Challenge

arXiv: https://arxiv.org/abs/2503.19800 · HTML: https://mlsanity.com/paper/2503.19800/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://mlsanity.com/paper/2402.01613/) (2024)
- [CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification](https://mlsanity.com/paper/2403.11904/) (2024)

## Built on by (most cited first, 4 of 4)

- [Ustnlp16 at SemEval-2025 Task 9: Improving Model Performance through Imbalance Handling and Focal Loss](https://mlsanity.com/paper/2505.00021/) (2025)
- [Efficient Text Classification with Conformal In-Context Learning](https://mlsanity.com/paper/2512.05732/) (2025)
- [BrightCookies at SemEval-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification](https://mlsanity.com/paper/2504.20703/) (2025)
- [PASTABench: Proactive Assessment of Sequential Trajectories for Agent Safety](https://mlsanity.com/paper/2609.28197/) (2026)

## Abstract (arXiv)

In this challenge, we explored text-based food hazard prediction with long tail distributed classes. The task was divided into two subtasks: (1) predicting whether a web text implies one of ten food-hazard categories and identifying the associated food category, and (2) providing a more fine-grained classification by assigning a specific label to both the hazard and the product. Our findings highlight that large language model-generated synthetic data can be highly effective for oversampling long-tail distributions. Furthermore, we find that fine-tuned encoder-only, encoder-decoder, and decoder-only systems achieve comparable maximum performance across both subtasks. During this challenge, we gradually released (under CC BY-NC-SA 4.0) a novel set of 6,644 manually labeled food-incident reports.