How Does Fine-tuning Affect the Geometry of Embedding Space: A Case Study on Isotropy (2021.findings-emnlp)
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| Challenge: | Existing methods for fine-tuning pre-trained language models are ineffective, despite their potential, pre-training models suffer from important weaknesses. |
| Approach: | They analyze the extent to which the isotropy of the embedding space changes after fine-tuning. |
| Outcome: | The proposed model improves the isotropy of embedding space after fine-tuning . the model can encode linguistic properties, but lacks the social bias needed to improve it . |
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