Distinguishability Calibration to In-Context Learning
- URL: http://arxiv.org/abs/2302.06198v3
- Date: Wed, 10 May 2023 09:16:53 GMT
- Title: Distinguishability Calibration to In-Context Learning
- Authors: Hongjing Li, Hanqi Yan, Yanran Li, Li Qian, Yulan He, Lin Gui
- Abstract summary: We propose a method to map a PLM-encoded embedding into a new metric space to guarantee the distinguishability of the resulting embeddings.
We also take the advantage of hyperbolic embeddings to capture the hierarchical relations among fine-grained class-associated token embedding.
- Score: 31.375797763897104
- License: http://creativecommons.org/licenses/by/4.0/
- 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. When using prompt-based learning for text
classification, the goal is to use a pre-trained language model (PLM) to
predict a missing token in a pre-defined template given an input text, which
can be mapped to a class label. However, PLMs built on the transformer
architecture tend to generate similar output embeddings, making it difficult to
discriminate between different class labels. The problem is further exacerbated
when dealing with classification tasks involving many fine-grained class
labels. In this work, we alleviate this information diffusion issue, i.e.,
different tokens share a large proportion of similar information after going
through stacked multiple self-attention layers in a transformer, by proposing a
calibration method built on feature transformations through rotation and
scaling to map a PLM-encoded embedding into a new metric space to guarantee the
distinguishability of the resulting embeddings. Furthermore, we take the
advantage of hyperbolic embeddings to capture the hierarchical relations among
fine-grained class-associated token embedding by a coarse-to-fine metric
learning strategy to enhance the distinguishability of the learned output
embeddings. Extensive experiments on the three datasets under various settings
demonstrate the effectiveness of our approach. Our code can be found at
https://github.com/donttal/TARA.
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