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In-context learning (ICL) in Large Language Models (LLMs) has emerged as a powerful new learning paradigm.
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.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David. 2014 · 2014
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interpreting gpt: the logit lens
nostalgebraist. 2020 · 2020
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
Earlier work this paper cites.
What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou. 2022 · 2022
Earlier work this paper cites.
Data distributional properties drive emergent in-context learning in transformers
Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, Pierre Richemond, James McClelland, and Felix Hill. 2022 · 2022
Earlier work this paper cites.
Why can gpt learn in-context? language models secretly perform gradient descent as meta optimizers
Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Zhifang Sui, and Furu Wei. 2022 · 2022
Cited alongside, same era.
Analyzing transformers in embedding space
Guy Dar, Mor Geva, Ankit Gupta, and Jonathan Berant. 2022 · 2022
Cited alongside, same era.
A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 2022
Cited alongside, same era.
The inductive bias of in-context learning: Rethinking pretraining example design
Yoav Levine, Noam Wies, Daniel Jannai, Dan Navon, Yedid Hoshen, and Amnon Shashua. 2022 · 2022
Cited alongside, same era.
Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022
Transformers learn in-context by gradient descent
Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov. 2022 · 2022
Later among the works it cites.
Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al. 2023 · 2023
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2023 · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
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Language models implement simple word2vec-style vector arithmetic
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Cited alongside, same era.
In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al. 2022 · 2022
Cited alongside, same era.
What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy Liang, and Gregory Valiant
Cited in the paper.
An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma
Cited in the paper.
Jack Merullo, Carsten Eickhoff, and Ellie Pavlick. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
Closest in time.