FLAT: Fusing layer representations for more efficient transfer learning in NLP-Reference-Cited by-同舟云学术

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FLAT: Fusing layer representations for more efficient transfer learning in NLP

Published:2024-11 Issue: Volume:179 Page:106631
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Zhu Wenqiang,Li Yang^ORCID,Liu Chunyan^ORCID,Zhao Yunlong^ORCID

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Ministry of Science and Technology of the People's Republic of China

National Major Science and Technology Projects of China

Publisher

Elsevier BV

Reference64 articles.

1. Intrinsic dimensionality explains the effectiveness of language model fine-tuning;Aghajanyan,2021

2. Layer normalization;Ba,2016

3. The second PASCAL recognising textual entailment challenge;Bar-Haim,2006

4. Bentivogli, L., Clark, P., Dagan, I., & Giampiccolo, D. (2009). The Fifth PASCAL Recognizing Textual Entailment Challenge. In TAC.

5. Language models are few-shot learners;Brown,2020

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