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Continual learning has emerged as an important research direction due to the infeasibility of retraining large language models (LLMs) from scratch in the event of new data availability.
Energy and policy considerations for deep learning in nlp
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 1906
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 1907
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2009
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Earlier work this paper cites.
The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English
Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, and Marc’Aurelio Ranzato. 2019 · 2019
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Linguistic knowledge and transferability of contextual representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith. 2019 · 2019
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A multiscale visualization of attention in the transformer model
Jesse Vig. 2019 · 2019
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Analyzing the structure of attention in a transformer language model
Jesse Vig and Yonatan Belinkov. 2019 · 2019
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Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov. 2019 · 2019
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
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Multi-stage pre-training for low-resource domain adaptation
Rong Zhang, Revanth Gangi Reddy, Md Arafat Sultan, Vittorio Castelli, Anthony Ferritto, Radu Florian, Efsun Sarioglu Kayi, Salim Roukos, Avi Sil, and Todd Ward. 2020 · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
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A survey of data augmentation approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021 · 2021
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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. 2021 · 2021
Cited alongside, same era.
mDAPT: Multilingual domain adaptive pretraining in a single model
Rasmus Kær Jørgensen, Mareike Hartmann, Xiang Dai, and Desmond Elliott. 2021 · 2021
Cited alongside, same era.
Carbon emissions and large neural network training
David A. Patterson, Joseph Gonzalez, Quoc V. Le, Chen Liang, Lluís-Miquel Munguía, Daniel Rothchild, David R. So, Maud Texier, and Jeff Dean. 2021 · 2021
Cited alongside, same era.
Towards a Cleaner Document-Oriented Multilingual Crawled Corpus
Julien Abadji, Pedro Ortiz Suarez, Laurent Romary, and Benoît Sagot. 2022 · 2022
Cited alongside, same era.
Computational historical linguistics and language diversity in South Asia
Openhathi series: An approach to build bilingual llms frugally
SarvamAI. 2023 · 2023
Later among the works it cites.
Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
Later among the works it cites.
To code, or not to code? exploring impact of code in pre-training
Viraat Aryabumi, Yixuan Su, Raymond Ma, Adrien Morisot, Ivan Zhang, Acyr Locatelli, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker. 2024 · 2024
Closest in time.
Lora learns less and forgets less
Dan Biderman, Jacob Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, and John P. Cunningham. 2024 · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Aryaman Arora, Adam Farris, Samopriya Basu, and Suresh Kolichala. 2022 · 2022
Cited alongside, same era.
No language left behind: Scaling human-centered machine translation
Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, and Loic Barrault et al. 2022 · 2022
Cited alongside, same era.
Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
Cited alongside, same era.
An empirical survey of data augmentation for limited data learning in NLP
Jiaao Chen, Derek Tam, Colin Raffel, Mohit Bansal, and Diyi Yang. 2023 · 2023
Cited alongside, same era.
Free dolly: Introducing the world’s first truly open instruction-tuned llm
Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023 · 2023
Cited alongside, same era.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023 · 2023
Cited alongside, same era.
Jay Gala, Pranjal A. Chitale, Raghavan AK, Varun Gumma, Sumanth Doddapaneni, Aswanth Kumar, Janki Nawale, Anupama Sujatha, Ratish Puduppully, Vivek Raghavan, Pratyush Kumar, Mitesh M. Khapra, Raj Dabre, and Anoop Kunchukuttan. 2023 · 2023
Cited alongside, same era.
Unsloth ai: Open source fine-tuning for llms
Daniel Han. 2023 · 2023
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, and Angela Fan et al. 2024 · 2024
Closest in time.
A framework for few-shot language model evaluation
Leo Gao, Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Alain Le Noac’h, Haonan Li, Kyle McDonell, Niklas Muennighoff, Chris Ociepa, Jason Phang, Laria Reynolds, Hailey Schoelkopf, Aviya Skowron, Lintang Sutawika, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou. 2024 · 2024
Closest in time.
Are large language model-based evaluators the solution to scaling up multilingual evaluation?
Rishav Hada, Varun Gumma, Adrian de Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali, and Sunayana Sitaram. 2024 · 2024
Closest in time.
Introducing meta llama 3: The most capable openly available llm to date
Meta. 2024 · 2024
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OpenAI. 2024 · 2024
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Include: Evaluating multilingual language understanding with regional knowledge
Angelika Romanou, Negar Foroutan, Anna Sotnikova, Zeming Chen, Sree Harsha Nelaturu, Shivalika Singh, Rishabh Maheshwary, Micol Altomare, and Mohamed A. Haggag et al. 2024 · 2024
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Llm see, llm do: Guiding data generation to target non-differentiable objectives
Luísa Shimabucoro, Sebastian Ruder, Julia Kreutzer, Marzieh Fadaee, and Sara Hooker. 2024 · 2024
Closest in time.
Will we run out of data? limits of llm scaling based on human-generated data
Pablo Villalobos, Anson Ho, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, and Marius Hobbhahn. 2024 · 2024
Closest in time.
Chain of lora: Efficient fine-tuning of language models via residual learning
Wenhan Xia, Chengwei Qin, and Elad Hazan. 2024 · 2024
Closest in time.
Galore: Memory-efficient llm training by gradient low-rank projection
Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, and Yuandong Tian. 2024 · 2024
Closest in time.
Investigating continual pretraining in large language models: Insights and implications
Çağatay Yıldız, Nishaanth Kanna Ravichandran, Prishruit Punia, Matthias Bethge, and Beyza Ermis. 2024 · 2024
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