Language Model Fine-tuning
Language Model Fine-Tuning is a machine learning adaptation procedure in which a pre-trained language model undergoes additional training on a domain-specific or task-specific dataset to refine its parameters for improved performance on targeted downstream applications.
839
Publications
116.9K
Citations
4.2K
Authors
600
Institutions
Publications per year
2017–2026
828
Parents
839
On the Dangers of Stochastic Parrots
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major et al. · 2021 · 5.6K citations · Full text
Computing, Computer Science, English +12
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang et al. · arXiv (Cornell University) · 2022 · 4.3K citations · Full text
1–5 of 839
4.2K
19
h-index
14
h-index
11
h-index
11
h-index
10
h-index
1–5 of 4.2K
600
Tsinghua University
Beijing, China
25 h-index
184 publications
36.4K citations
Google (United States)
Mountain View, United States
20 h-index
93 publications
9.5K citations
University of Washington
Seattle, United States
17 h-index
46 publications
16.8K citations
Stanford University
Stanford, United States
16 h-index
43 publications
9K citations

Peking University
Beijing, China
16 h-index
46 publications
3.2K citations
Meta (Israel)
Tel Aviv, Israel
15 h-index
52 publications
5.2K citations
Carnegie Mellon University
Pittsburgh, United States
14 h-index
25 publications
3.1K citations
Massachusetts Institute of Technology
Cambridge, United States
14 h-index
34 publications
2.3K citations
1–8 of 600
Repository · 318 publications · 44.4K citations
77
h-index
Conference · 24 publications · 1.3K citations
17
h-index
Conference · 16 publications · 808 citations
15
h-index
Conference · 15 publications · 1.7K citations
14
h-index
10 publications · 405 citations
10
h-index
1–5