ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866914754798288896 |
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| author | Zhu, Yaxin Zamani, Hamed |
| author_facet | Zhu, Yaxin Zamani, Hamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_09649 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification Zhu, Yaxin Zamani, Hamed Machine Learning Computation and Language 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. |
| title | ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2311.09649 |