Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning

21 December 2022

Papers citing "Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning"

8 / 8 papers shown

Title
Augmented Language Models: a Survey Grégoire Mialon Roberto Dessì Maria Lomeli Christoforos Nalmpantis Ramakanth Pasunuru ... Jane Dwivedi-Yu Asli Celikyilmaz Edouard Grave Yann LeCun Thomas Scialom LRM KELM 41 367 0 15 Feb 2023
Large Language Models are Zero-Shot Reasoners Takeshi Kojima S. Gu Machel Reid Yutaka Matsuo Yusuke Iwasawa ReLM LRM 322 4,077 0 24 May 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 313 11,953 0 04 Mar 2022
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Jason W. Wei Xuezhi Wang Dale Schuurmans Maarten Bosma Brian Ichter F. Xia Ed H. Chi Quoc Le Denny Zhou LM&Ro LRM AI4CE ReLM 367 8,495 0 28 Jan 2022
Internet-Augmented Dialogue Generation M. Komeili Kurt Shuster Jason Weston RALM 238 280 0 15 Jul 2021
Explaining Answers with Entailment Trees Bhavana Dalvi Peter Alexander Jansen Oyvind Tafjord Zhengnan Xie Hannah Smith Leighanna Pipatanangkura Peter Clark ReLM FAtt LRM 239 184 0 17 Apr 2021
Distilling Knowledge from Reader to Retriever for Question Answering Gautier Izacard Edouard Grave RALM 185 251 0 08 Dec 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 243 4,469 0 23 Jan 2020