Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have shown incredible performance in completing various real-world tasks.
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.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Earlier work this paper cites.
Harnessing deep neural networks with logic rules
Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, and Eric Xing. 2016 · 2016
Earlier work this paper cites.
Sequence-level knowledge distillation
Yoon Kim and Alexander M Rush. 2016 · 2016
Earlier work this paper cites.
Patient knowledge distillation for bert model compression
Siqi Sun, Yu Cheng, Zhe Gan, and Jingjing Liu. 2019 · 2019
Earlier work this paper cites.
Learning from rules generalizing labeled exemplars
Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, and Sunita Sarawagi. 2020 · 2020
Earlier work this paper cites.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
Earlier work this paper cites.
Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. 2022 · 2022
Earlier work this paper cites.
LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Earlier work this paper cites.
What makes good in-context examples for GPT-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022 · 2022
Earlier work this paper cites.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022b · 2022
Earlier work this paper cites.
Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2022 · 2022
Earlier work this paper cites.
ChatGPT: Optimizing Language Models for Dialogue
OpenAI. 2022 · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2022 · 2022
Cited alongside, same era.
Learning by distilling context
Charlie Snell, Dan Klein, and Ruiqi Zhong. 2022 · 2022
Cited alongside, same era.
Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, et al. 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
Gpt-4 technical report
OpenAI. 2023 · 2023
Closest in time.
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. 2023 · 2023
Closest in time.
Stanford alpaca: an instruction-following llama model (2023)
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto. 2023 · 2023
Closest in time.
Transformers learn in-context by gradient descent
Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov. 2023 · 2023
Closest in time.
Label words are anchors: An information flow perspective for understanding in-context learning
Lean Wang, Lei Li, Damai Dai, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, and Xu Sun. 2023a · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Ground-truth labels matter: A deeper look into input-label demonstrations
Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, Hyunsoo Cho, Hwiyeol Jo, Sang-Woo Lee, Sang-goo Lee, and Taeuk Kim. 2022 · 2022
Cited alongside, same era.
Why can gpt learn in-context? language models secretly perform gradient descent as meta-optimizers
Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Shuming Ma, Zhifang Sui, and Furu Wei. 2023 · 2023
Cited alongside, same era.
Do models really learn to follow instructions? an empirical study of instruction tuning
Po-Nien Kung and Nanyun Peng. 2023 · 2023
Cited alongside, same era.
Diverse demonstrations improve in-context compositional generalization
Itay Levy, Ben Bogin, and Jonathan Berant. 2023 · 2023
Cited alongside, same era.
Bo Li, Gexiang Fang, Yang Yang, Quansen Wang, Wei Ye, Wen Zhao, and Shikun Zhang. 2023 · 2023
Cited alongside, same era.
Goat: Fine-tuned llama outperforms gpt-4 on arithmetic tasks
Tiedong Liu and Bryan Kian Hsiang Low. 2023 · 2023
Cited alongside, same era.
Jailbreaking chatgpt via prompt engineering: An empirical study
Yi Liu, Gelei Deng, Zhengzi Xu, Yuekang Li, Yaowen Zheng, Ying Zhang, Lida Zhao, Tianwei Zhang, and Yang Liu. 2023 · 2023
Cited alongside, same era.
Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang, Xin Zhang, Shen Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang, et al. 2023 · 2023
Closest in time.
Xuansheng Wu, Wenlin Yao, Jianshu Chen, Xiaoman Pan, Xiaoyang Wang, Ninghao Liu, and Dong Yu. 2023 · 2023
Closest in time.
Backdooring instruction-tuned large language models with virtual prompt injection
Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, and Hongxia Jin. 2023 · 2023
Closest in time.
Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson. 2023 · 2023
Closest in time.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2024 · 2024
Closest in time.
A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, et al. 2024 · 2024
Closest in time.
Minillm: Knowledge distillation of large language models
Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. 2024 · 2024
Closest in time.
Lima: Less is more for alignment
Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al. 2024 · 2024
Closest in time.