How to Estimate Whether You Have Found Several Needles in a Haystack: Measuring Calibration in Multi-Label Text Classification

arXiv:2609.26468 · cs.CL · Submitted 2026-09-22 · Read on arXiv

cs.CL

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/sophiehenning/multilabel-classification-calibration

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 124× in this paper · explained in 7 episodes
• RAG — 26× in this paper · explained in 3 episodes
• F1 Score — 8× in this paper · explained in 3 episodes
• Empirical Methods in Natural Language Processing — 6× in this paper
• Expected Calibration Error — 6× in this paper
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• In-Context Learning — 6× in this paper · explained in 4 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Copula — 4× in this paper · explained in 2 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Hierarchy-aware Biased Bound Margin — 2× in this paper
• Loss Function — 2× in this paper · explained in 2 episodes
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Dynamic Weighting — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• In-Context Learning for Extreme Multi-Label Classification
• The Faiss library
• The Llama 3 Herd of Models
• Are We Really Making Much Progress in Text Classification? A Comparative Review
• Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance
• Gated Recurrent Neural Network Approach for Multilabel Emotion Detection in Microblogs
• Labels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers?
• Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
• Larger language models do in-context learning differently
• Qwen2.5 Technical Report

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