Cluster-Guided Label Generation in Extreme Multi-Label Classification (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing classification-based models are poorly per-form for tail labels and ignore semantic relations among labels. |
| Approach: | They propose to guide label generation using label cluster information to hierarchically generate lower-level labels. |
| Outcome: | The proposed model outperforms classification and generation baselines on tail labels and improves in four popular XMC benchmarks. |
Similar Papers
XMC-Agent : Dynamic Navigation over Scalable Hierarchical Index for Incremental Extreme Multi-label Classification (2024.findings-acl)
Copied to clipboard
Yanjiang Liu, Tianyun Zhong, Yaojie Lu, Hongyu Lin, Ben He, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing methods for XMC struggle with the growing set of labels due to their static label assumptions, and embedding-based methods struggle with complex mapping relationships due to late interaction paradigm. |
| Approach: | They propose a large language model (LLM) powered agent framework for extreme multi-label classification, XMC-Agent, which can effectively learn, manage and predict the extremely large and dynamically increasing set of labels. |
| Outcome: | The proposed framework can learn, manage and predict the extremely large and dynamically growing set of labels and achieves state-of-the-art performance on three standard datasets. |
Open Vocabulary Extreme Classification Using Generative Models (2022.findings-acl)
Copied to clipboard
Daniel Simig, Fabio Petroni, Pouya Yanki, Kashyap Popat, Christina Du, Sebastian Riedel, Majid Yazdani
| Challenge: | Extreme multi-label classification (XMC) aims at tagging content with subset of labels from an extremely large label set. |
| Approach: | They propose a model that predicts a set of labels outside of the known vocabulary by using a loss-dependent loss-based loss-free model. |
| Outcome: | The proposed model can predict labels outside the known vocabulary while performing on par with state-of-the-art solutions for known labels. |
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent work in XMC addresses this problem using deep encoders that project text descriptions to an embedding space suitable for recovering the closest labels. |
| Approach: | They propose a method that uses a shallow transformer encoder to combine text-based embeddings, label centroids and learnable free vectors to improve XMC efficiency. |
| Outcome: | The proposed method achieves state-of-the-art in several public benchmarks of different sizes and domains while keeping the model efficient. |
Enhancing Extreme Multi-Label Text Classification: Addressing Challenges in Model, Data, and Evaluation (2023.emnlp-industry)
Copied to clipboard
Dan Li, Zi Long Zhu, Janneke van de Loo, Agnes Masip Gomez, Vikrant Yadav, Georgios Tsatsaronis, Zubair Afzal
| Challenge: | Existing approaches to extreme multi-label text classification face inherent challenges in terms of model, data, and evaluation. |
| Approach: | They propose a label ranking model as an alternative to the conventional SciBERT-based classification model and an active learning-based pipeline that addresses the data scarcity of new labels during the update of a classification system. |
| Outcome: | The proposed model enables efficient handling of large-scale labels and accommodates new labels. |
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning (2025.findings-naacl)
Copied to clipboard
| Challenge: | Extreme multi-label classification (OXMC) is a challenging and critical task in natural language processing. |
| Approach: | They propose to use PUSL to reframe OXMC as an infinite keyphrase generation task . they propose to adopt evaluation metrics to reliably assess OXML models with incomplete ground truths. |
| Outcome: | The proposed approach improves on a highly imbalanced e-commerce dataset with missing labels . it generates 30% more unique labels and 72% of its predictions align with actual user queries . |
Extreme Zero-Shot Learning for Extreme Text Classification (2022.naacl-main)
Copied to clipboard
| Challenge: | Experimental results show that MACLR achieves superior performance compared to other baseline methods. |
| Approach: | They propose to pre-train Transformer-based encoders with self-supervised contrastive losses to learn the semantic embeddings of instances and labels with raw text. |
| Outcome: | The proposed method improves on the EZ-XMC model with a limited number of ground-truth positive pairs. |
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems (N19-1)
Copied to clipboard
| Challenge: | Experimental results show that extreme multi-label learning improves label prediction quality by 3% to 5% in three of the 5 tasks and is competitive in the others. |
| Approach: | They propose a submodular maximization framework with linear cost to find informative labels which are most relevant to other labels yet least redundant with each other. |
| Outcome: | The proposed model improves label prediction quality by 3% to 5% in three of the 5 tasks and is competitive in the others. |
ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing research has focused on fully supervised XMC, but real-world scenarios often lack supervision signals, highlighting the importance of zero-shot settings. |
| Approach: | They propose a framework that generates a set of candidate labels through in-context learning and then reranks them. |
| Outcome: | The proposed framework advances state-of-the-art on two diverse public benchmarks. |
Open-world Multi-label Text Classification with Extremely Weak Supervision (2024.emnlp-main)
Copied to clipboard
| Challenge: | Similar single-label XWS settings cannot be easily adapted for multi-l label classification. |
| Approach: | They propose a novel method for open-world multi-label text classification under extremely weak supervision where the user provides a brief description without any labels or ground-truth label space. |
| Outcome: | The proposed method exhibits a remarkable increase in ground-truth label space coverage on various datasets. |
Large Language Models Do Multi-Label Classification Differently (2025.emnlp-main)
Copied to clipboard
| 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. |