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MetaPrompting: Learning to Learn Better Prompts. (arXiv:2209.11486v1 [cs.CL])
Sept. 26, 2022, 1:15 a.m. | Yutai Hou, Hongyuan Dong, Xinghao Wang, Bohan Li, Wanxiang Che
cs.CL updates on arXiv.org arxiv.org
Prompting method is regarded as one of the crucial progress for few-shot
nature language processing. Recent research on prompting moves from discrete
tokens based ``hard prompts'' to continuous ``soft prompts'', which employ
learnable vectors as pseudo prompt tokens and achieve better performance.
Though showing promising prospects, these soft-prompting methods are observed
to rely heavily on good initialization to take effect. Unfortunately, obtaining
a perfect initialization for soft prompts requires understanding of inner
language models working and elaborate design, which is …
More from arxiv.org / cs.CL updates on arXiv.org
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