PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning

Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901

Earlier work this paper cites.

Prototypical networks for few-shot learning

Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017

Earlier work this paper cites.

Attention is all you need

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017

Earlier work this paper cites.

Nl2bash: A corpus and semantic parser for natural language interface to the linux operating system

Xi Victoria Lin, Chenglong Wang, Luke Zettlemoyer, and Michael D Ernst. 2018 · 2018

Earlier work this paper cites.

Unified language model pre-training for natural language understanding and generation

Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019

Earlier work this paper cites.

Parameter-efficient transfer learning for nlp

Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019

Earlier work this paper cites.

Retrieval-augmented generation for knowledge-intensive nlp tasks

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2020

Earlier work this paper cites.

Program synthesis with large language models

Original

Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. 2021 · 2021

Earlier work this paper cites.

Training verifiers to solve math word problems

Original

Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021

Earlier work this paper cites.

Towards a unified view of parameter-efficient transfer learning

Original

Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2021 · 2021

Earlier work this paper cites.

Lora: Low-rank adaptation of large language models

Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021 · 2021

Earlier work this paper cites.

Finetuned language models are zero-shot learners

Original

Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021

Earlier work this paper cites.

Decoupling knowledge from memorization: Retrieval-augmented prompt learning

Xiang Chen, Lei Li, Ningyu Zhang, Xiaozhuan Liang, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, and Huajun Chen. 2022 · 2022

Earlier work this paper cites.

Promptagator: Few-shot dense retrieval from 8 examples

Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith Hall, and Ming-Wei Chang. 2022 · 2022

Earlier work this paper cites.

P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks

Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2022 · 2022

Earlier work this paper cites.

Learning to retrieve prompts for in-context learning

Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022 · 2022

Cited alongside, same era.

Two-stage llm fine-tuning with less specialization and more generalization

Original

Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, and Sanjiv Kumar. 2022 · 2022

Cited alongside, same era.

Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022

Cited alongside, same era.

Prompt compression and contrastive conditioning for controllability and toxicity reduction in language models

David Wingate, Mohammad Shoeybi, and Taylor Sorensen. 2022 · 2022

Cited alongside, same era.

Large language models meet nl2code: A survey

Original

Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Yongji Wang, and Jian-Guang Lou. 2022 · 2022

Recomp: Improving retrieval-augmented lms with context compression and selective augmentation

Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2023 · 2023

Later among the works it cites.

Aspen: High-throughput lora fine-tuning of large language models with a single gpu

Original

Zhengmao Ye, Dengchun Li, Jingqi Tian, Tingfeng Lan, Jie Zuo, Lei Duan, Hui Lu, Yexi Jiang, Jian Sha, Ke Zhang, et al. 2023 · 2023

Later among the works it cites.

Minigpt-4: Enhancing vision-language understanding with advanced large language models

Original

Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023

Later among the works it cites.

Qlora: Efficient finetuning of quantized llms

Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2024 · 2024

Closest in time.

Conline: Complex code generation and refinement with online searching and correctness testing

Original