Is it me or are deep learning papers getting more and more hyperbolic in their use of language? Check out the first sentence in the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-
tuned on a downstream task, has emerged as a powerful technique in natural language
processing (NLP).
You could re-write that without the pomp:
Transfer learning is a technique where a model is first pre-trained on a data-rich task before being finetuned on a downstream task.
And you lose none of the meaning for dropping the "powerful" bombast. What is "powerful" anyway? Is this a research paper or a social media post?
This is really something I've noticed more and more lately -see e.g. the recent paper on the blindness of vision LLMs:
Transfer learning is a technique where a model is first pre-trained on a data-rich task before being finetuned on a downstream task.
And you lose none of the meaning for dropping the "powerful" bombast. What is "powerful" anyway? Is this a research paper or a social media post?
This is really something I've noticed more and more lately -see e.g. the recent paper on the blindness of vision LLMs:
https://vlmsareblind.github.io/
Whence I quote (the abstract):
And many more in the body. What's with all that? Aren't results enough to draw attention to your research work anymore?