Papers with Multi-label classification
Large Language Models Do Multi-Label Classification Differently (2025.emnlp-main)
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| Challenge: | Multi-label classification is prevalent in real-world settings, but the behavior of Large Language Models (LLMs) in this setting is understudied. |
| Approach: | They propose to use initial probability distributions to analyze output distributions of LLMs at each label generation step to find out how LLM models perform multi-label classification. |
| Outcome: | The proposed methods improve alignment and predictive performance over existing methods. |
Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)
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| Challenge: | Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods . |
| Approach: | They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems. |
| Outcome: | The proposed method is competitive to state-of-the-art methods on benchmark datasets. |
SGM: Sequence Generation Model for Multi-label Classification (C18-1)
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| Challenge: | Existing methods ignore the correlations between labels and different parts of the text can contribute differently for predicting different labels. |
| Approach: | They propose to view the multi-label classification task as a sequence generation problem and apply a decoder-based sequence generation model to solve it. |
| Outcome: | The proposed methods outperform previous work by a substantial margin. |
CCG: Rare-Label Prediction via Neural SEM–Driven Causal Game (2025.findings-emnlp)
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| Challenge: | Multi-label classification (MLC) faces persistent challenges from label imbalance, spurious correlations, distribution shifts, especially in rare label prediction. |
| Approach: | They propose a Causal Cooperative Game framework that models multi-player cooperative process for multi-label classification. |
| Outcome: | The proposed framework improves rare label prediction and overall robustness compared to baselines. |
A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification (P19-1)
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| Challenge: | Multi-label classification (MLC) aims to assign multiple labels to each sample. |
| Approach: | They propose a sequence-to-set model that is trained via reinforcement learning and rewards feedback independent of the label order. |
| Outcome: | The proposed model outperforms baseline models and reduces sensitivity to label order. |