mlsanityData to 7 Oct 2026

ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification

arXiv 2311.09649 ↗First posted 16 Nov 2023cs.LG cs.CLcited by 5

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Abstract (arXiv)

This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space. While existing research has primarily focused on fully supervised XMC, real-world scenarios often lack supervision signals, highlighting the importance of zero-shot settings. Given the large label space, utilizing in-context learning approaches is not trivial. We address this issue by introducing In-Context Extreme Multilabel Learning (ICXML), a two-stage framework that cuts down the search space by generating a set of candidate labels through incontext learning and then reranks them. Extensive experiments suggest that ICXML advances the state of the art on two diverse public benchmarks.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 28 of its 42 references that are in the map. First posted on arXiv 16 Nov 2023.

Builds on

28 of 42 references.

References · 28

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  5. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  6. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  7. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  8. 2021Learning To Retrieve Prompts for In-Context Learningcited by 641
  9. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 599
  10. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  11. 2022Complexity-Based Prompting for Multi-Step Reasoningcited by 387
  12. 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 356
16 more
  1. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  2. 2021Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Samplingcited by 238
  3. 2022Impact of Pretraining Term Frequencies on Few-Shot Reasoningcited by 142
  4. 2019Large-Scale Multi-Label Text Classification on EU Legislationcited by 104
  5. 2023UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluationcited by 74
  6. 2023Dr.ICL: Demonstration-Retrieved In-context Learningcited by 71
  7. 2022Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrationscited by 70
  8. 2021LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classificationcited by 61
  9. 2023Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrationscited by 42
  10. 2021ECLARE: Extreme Classification with Label Graph Correlationscited by 38
  11. 2021DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documentscited by 37
  12. 2022NGAME: Negative Mining-aware Mini-batching for Extreme Classificationcited by 32
  13. 2023Large Language Models are Strong Zero-Shot Retrievercited by 25
  14. 2021Extreme Zero-Shot Learning for Extreme Text Classificationcited by 15
  15. 2022Open Vocabulary Extreme Classification Using Generative Modelscited by 13
  16. 2023SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classificationcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2025From Haystack to Needle: Label Space Reduction for Zero-shot Classificationcited by 2
  2. 2025From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industrycited by 1
  3. 2024Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rankcited by 0
  4. 2025Large Language Models as Medical Codes Selectors: a benchmark using the International Classification of Primary Carecited by 0
  5. 2026How to Estimate Whether You Have Found Several Needles in a Haystack: Measuring Calibration in Multi-Label Text Classificationcited by 0