Abstract
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 Multi-label Learning (ICXML), a two-stage framework that cuts down the search space by generating a set of candidate labels through in-context learning and then reranks them. Extensive experiments suggest that ICXML advances the state of the art on two diverse public benchmarks.- Anthology ID:
- 2024.findings-naacl.134
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2086–2098
- Language:
- URL:
- https://aclanthology.org/2024.findings-naacl.134
- DOI:
- Cite (ACL):
- Yaxin Zhu and Hamed Zamani. 2024. ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2086–2098, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification (Zhu & Zamani, Findings 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.findings-naacl.134.pdf