# CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification

arXiv: https://arxiv.org/abs/2403.11904 · HTML: https://mlsanity.com/paper/2403.11904/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks](https://mlsanity.com/paper/2205.10726/) (2022)

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

- [SemEval-2025 Task 9: The Food Hazard Detection Challenge](https://mlsanity.com/paper/2503.19800/) (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)
- [Improving Narrative Classification and Explanation via Fine Tuned Language Models](https://mlsanity.com/paper/2509.04077/) (2025)

## Abstract (arXiv)

Contaminated or adulterated food poses a substantial risk to human health. Given sets of labeled web texts for training, Machine Learning and Natural Language Processing can be applied to automatically detect such risks. We publish a dataset of 7,546 short texts describing public food recall announcements. Each text is manually labeled, on two granularity levels (coarse and fine), for food products and hazards that the recall corresponds to. We describe the dataset and benchmark naive, traditional, and Transformer models. Based on our analysis, Logistic Regression based on a tf-idf representation outperforms RoBERTa and XLM-R on classes with low support. Finally, we discuss different prompting strategies and present an LLM-in-the-loop framework, based on Conformal Prediction, which boosts the performance of the base classifier while reducing energy consumption compared to normal prompting.