CICLe: Conformal In-Context Learning for Large-scale Multi-Class Food Risk Classification

Core Concepts

Conformal Prediction enhances few-shot prompting for accurate and energy-efficient multi-class classification in the food domain.

Abstract

Contaminated or adulterated food poses significant health risks, motivating the development of Machine Learning (ML) and Natural Language Processing (NLP) solutions. A dataset of 7,546 short texts describing public food recall announcements is manually labeled on two granularity levels for food products and hazards. Logistic Regression outperforms RoBERTa and XLM-R on classes with low support. Few-shot prompting strategies with GPT-3.5 are discussed, including a novel LLM-in-the-loop framework based on Conformal Prediction (CP). Traditional ML classifiers and Transformers are benchmarked, revealing the potential of traditional classifiers on this dataset. The heavy class imbalance in the data presents challenges for classification tasks. The study highlights the importance of prompt design and sample order in few-shot prompting approaches.

Stats

We publish a dataset of 7,546 short texts describing public food recall announcements.
Logistic Regression based on a tf-idf representation outperforms RoBERTa and XLM-R on classes with low support.
The dataset consists of 7,546 short texts written in 6 languages.
TF-IDF-SVM shows good performance on high-support classes but struggles with low-support classes.
GPT-ALL performs comparably to TF-IDF-LR and RoBERTa in hazard-category but excels in product-category.

Quotes

"Contaminated or adulterated food poses a substantial risk to human health."
"We present the first dataset for text classification of food products and hazards on two levels of granularity."
"Logistic Regression based on a tf-idf representation outperforms RoBERTa and XLM-R on classes with low support."
"GPT-CICLe boosts the performance of the base classifier by detecting insecure samples using CP."
"The study highlights the potential of traditional classifiers on this dataset."

Key Insights Distilled From

CICLe

by Korbinian Ra... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11904.pdf

Deeper Inquiries

How can Conformal Prediction be further optimized for multi-class prediction problems?

Conformal Prediction (CP) can be enhanced for multi-class prediction by focusing on several key strategies:

Improved Non-Conformity Measures: Developing more sophisticated non-conformity measures that better capture the uncertainty of the model's predictions across multiple classes.
Enhanced Set Selection: Refining the process of selecting sets of predicted classes to ensure higher accuracy and coverage, especially in scenarios with a large number of classes.
Adaptive Alpha Values: Experimenting with different alpha values to strike a balance between set accuracy and length based on specific dataset characteristics and model performance.
Integration with Ensemble Methods: Combining CP with ensemble methods to leverage diverse models' predictions and enhance the robustness of multi-class predictions.

What are the implications of heavy class imbalance in datasets for machine learning models?

Heavy class imbalance in datasets poses several challenges for machine learning models:

Bias Towards Majority Classes: Models tend to perform well on majority classes while struggling with minority ones, leading to skewed results.
Reduced Generalization: Imbalanced data may hinder a model's ability to generalize effectively, as it might not learn enough from underrepresented classes.
Misleading Evaluation Metrics: Traditional evaluation metrics like accuracy can be misleading due to imbalanced distributions, requiring alternative metrics like F1-score or precision-recall curves.
Sampling Biases: Imbalance can introduce sampling biases during training, affecting how well a model learns patterns from different classes.

How can prompt design impact the performance of large language models like GPT-3.5?

Prompt design plays a crucial role in influencing the performance of large language models such as GPT-3.5:

Context Relevance: Crafting prompts that provide relevant context helps guide the LLM towards generating accurate responses aligned with the input information.
Sample Selection: Choosing appropriate few-shot examples within prompts enhances an LLM's understanding by exposing it to diverse instances related to various classes or concepts.
Ordering Strategy: The order in which samples are presented within prompts affects how well an LLM grasps underlying patterns; strategic ordering can improve comprehension and predictive accuracy.
Length Optimization: Balancing prompt length is essential; concise yet informative prompts prevent overwhelming an LLM while ensuring it receives adequate details for effective decision-making.