@inproceedings{li-etal-2023-distinguishability,
title = "Distinguishability Calibration to In-Context Learning",
author = "Li, Hongjing and
Yan, Hanqi and
Li, Yanran and
Qian, Li and
He, Yulan and
Gui, Lin",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.102",
doi = "10.18653/v1/2023.findings-eacl.102",
pages = "1385--1397",
abstract = "Recent years have witnessed increasing interests in prompt-based learning in which models can be trained on only a few annotated instances, making them suitable in low-resource settings. It is even challenging in fine-grained classification as the pre-trained language models tend to generate similar output embedding which makes it difficult to discriminate for the prompt-based classifier. In this work, we alleviate this information diffusion issue by proposing a calibration method based on a transformation which rotates the embedding feature into a new metric space where we adapt the ratio of each dimension to a uniform distribution to guarantee the distinguishability of learned embeddings. Furthermore, we take the advantage of hyperbolic embedding to capture the relation between dimensions by a coarse-fine metric learning strategy to enhance interpretability. Extensive experiments on the three datasets under various settings demonstrate the effectiveness of our approach.",
}
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<abstract>Recent years have witnessed increasing interests in prompt-based learning in which models can be trained on only a few annotated instances, making them suitable in low-resource settings. It is even challenging in fine-grained classification as the pre-trained language models tend to generate similar output embedding which makes it difficult to discriminate for the prompt-based classifier. In this work, we alleviate this information diffusion issue by proposing a calibration method based on a transformation which rotates the embedding feature into a new metric space where we adapt the ratio of each dimension to a uniform distribution to guarantee the distinguishability of learned embeddings. Furthermore, we take the advantage of hyperbolic embedding to capture the relation between dimensions by a coarse-fine metric learning strategy to enhance interpretability. Extensive experiments on the three datasets under various settings demonstrate the effectiveness of our approach.</abstract>
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%0 Conference Proceedings
%T Distinguishability Calibration to In-Context Learning
%A Li, Hongjing
%A Yan, Hanqi
%A Li, Yanran
%A Qian, Li
%A He, Yulan
%A Gui, Lin
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F li-etal-2023-distinguishability
%X Recent years have witnessed increasing interests in prompt-based learning in which models can be trained on only a few annotated instances, making them suitable in low-resource settings. It is even challenging in fine-grained classification as the pre-trained language models tend to generate similar output embedding which makes it difficult to discriminate for the prompt-based classifier. In this work, we alleviate this information diffusion issue by proposing a calibration method based on a transformation which rotates the embedding feature into a new metric space where we adapt the ratio of each dimension to a uniform distribution to guarantee the distinguishability of learned embeddings. Furthermore, we take the advantage of hyperbolic embedding to capture the relation between dimensions by a coarse-fine metric learning strategy to enhance interpretability. Extensive experiments on the three datasets under various settings demonstrate the effectiveness of our approach.
%R 10.18653/v1/2023.findings-eacl.102
%U https://aclanthology.org/2023.findings-eacl.102
%U https://doi.org/10.18653/v1/2023.findings-eacl.102
%P 1385-1397
Markdown (Informal)
[Distinguishability Calibration to In-Context Learning](https://aclanthology.org/2023.findings-eacl.102) (Li et al., Findings 2023)
ACL
- Hongjing Li, Hanqi Yan, Yanran Li, Li Qian, Yulan He, and Lin Gui. 2023. Distinguishability Calibration to In-Context Learning. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1385–1397, Dubrovnik, Croatia. Association for Computational Linguistics.