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This paper conducts an empirical survey on transferability estimation methods for selecting pre-trained language models (PLMs) without brute-force fine-tuning. It reviews existing methods, highlights the strengths and weaknesses of various approaches, and emphasizes the need for efficient estimation techniques to determine the most suitable PLM for a target task. The findings suggest that model similarity-based methods generally perform well, while training-free methods offer faster estimations, ultimately
- Author
- wenhesun576
- Language
- EN