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As open-weight large language models (LLMs) achieve ever more impressive performances across a wide range of tasks in English, practitioners aim to adapt these models to different languages.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Animating rotation with quaternion curves
Ken Shoemake. 1985 · 1985
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Catastrophic forgetting in connectionist networks
Robert M French. 1999 · 1999
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Europarl: A parallel corpus for statistical machine translation
Philipp Koehn. 2005 · 2005
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Query-driven active surveying for collective classification
Galileo Mark Namata, Ben London, Lise Getoor, and Bert Huang. 2012 · 2012
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An empirical investigation of catastrophic forgeting in gradient-based neural networks
Ian J. Goodfellow, Mehdi Mirza, Xia Da, Aaron C. Courville, and Yoshua Bengio. 2014 · 2014
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European Union language resources in Sketch Engine
Vít Baisa, Jan Michelfeit, Marek Medveď, and Miloš Jakubíček. 2016 · 2016
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017 · 2017
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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
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XNLI: Evaluating cross-lingual sentence representations
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov. 2018 · 2018
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Measuring catastrophic forgetting in neural networks
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler L. Hayes, and Christopher Kanan. 2018 · 2018
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SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Taku Kudo and John Richardson. 2018 · 2018
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MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 2019
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Openwebtext corpus
Aaron Gokaslan, Vanya Cohen, Ellie Pavlick, and Stefanie Tellex. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, and Gregory Wayne. 2019 · 2019
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PAWS-X: A cross-lingual adversarial dataset for paraphrase identification
Yinfei Yang, Yuan Zhang, Chris Tar, and Jason Baldridge. 2019 · 2019
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HellaSwag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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EXAMS: A multi-subject high school examinations dataset for cross-lingual and multilingual question answering
Momchil Hardalov, Todor Mihaylov, Dimitrina Zlatkova, Yoan Dinkov, Ivan Koychev, and Preslav Nakov. 2020 · 2020
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Mixout: Effective regularization to finetune large-scale pretrained language models
Cheolhyoung Lee, Kyunghyun Cho, and Wanmo Kang. 2020 · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
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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 · 2021
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Winogrande: an adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021 · 2021
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mT5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021 · 2021
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Fusing finetuned models for better pretraining
Leshem Choshen, Elad Venezian, Noam Slonim, and Yoav Katz. 2022 · 2022
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How does gpt obtain its ability? tracing emergent abilities of language models to their sources
Hao Fu, Yao; Peng and Tushar Khot. 2022 · 2022
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Fast vocabulary transfer for language model compression
Leonidas Gee, Andrea Zugarini, Leonardo Rigutini, and Paolo Torroni. 2022 · 2022
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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. 2022 · 2022
Cited alongside, same era.
Branch-train-merge: Embarrassingly parallel training of expert language models
Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A. Smith, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda. 2022 · 2022
Cited alongside, same era.
Merging models with fisher-weighted averaging
Michael Matena and Colin Raffel. 2022 · 2022
Cited alongside, same era.
Openhermes 2.5: An open dataset of synthetic data for generalist llm assistants
Teknium. 2023 · 2023
Later among the works it cites.
D4: improving LLM pretraining via document de-duplication and diversification
Kushal Tirumala, Daniel Simig, Armen Aghajanyan, and Ari Morcos. 2023 · 2023
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Redpajama: an open dataset for training large language models
Together.ai. 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, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Overcoming catastrophic forgetting in massively multilingual continual learning
Genta Indra Winata, Lingjue Xie, Karthik Radhakrishnan, Shijie Wu, Xisen Jin, Pengxiang Cheng, Mayank Kulkarni, and Daniel Preotiuc-Pietro. 2023 · 2023
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Effect of scale on catastrophic forgetting in neural networks
Vinay Venkatesh Ramasesh, Aitor Lewkowycz, and Ethan Dyer. 2022 · 2022
Cited alongside, same era.
Fine-tuned language models are continual learners
Thomas Scialom, Tuhin Chakrabarty, and Smaranda Muresan. 2022 · 2022
Cited alongside, same era.
Introducing the CURLICAT corpora: Seven-language domain specific annotated corpora from curated sources
Tamás Váradi, Bence Nyéki, Svetla Koeva, Marko Tadić, Vanja Štefanec, Maciej Ogrodniczuk, Bartłomiej Nitoń, Piotr Pęzik, Verginica Barbu Mititelu, Elena Irimia, Maria Mitrofan, Dan Tufi · 2022
Cited alongside, same era.
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt. 2022 · 2022
Cited alongside, same era.
The belebele benchmark: a parallel reading comprehension dataset in 122 language variants
Lucas Bandarkar, Davis Liang, Benjamin Muller, Mikel Artetxe, Satya Narayan Shukla, Donald Husa, Naman Goyal, Abhinandan Krishnan, Luke Zettlemoyer, and Madian Khabsa. 2023 · 2023
Cited alongside, same era.
Code alpaca: An instruction-following llama model for code generation
S. Chaudhary. 2023 · 2023
Cited alongside, same era.
MultilingualSIFT: Multilingual Supervised Instruction Fine-tuning
Zhihong Chen, Shuo Yan, Juhao Liang, Feng Jiang, Xiangbo Wu, Fei Yu, Guiming Hardy Chen, Junying Chen, Hongbo Zhang, Li Jianquan, Wan Xiang, and Benyou Wang. 2023 · 2023
Cited alongside, same era.
Amplify-instruct: Synthetically generated diverse multi-turn conversations for efficient llm training
Luigi Daniele and Suphavadeeprasit. 2023 · 2023
Cited alongside, same era.
lo-fi: distributed fine-tuning without communication
Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael Rabbat, and Ari S. Morcos. 2023 · 2023
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Doremi: Optimizing data mixtures speeds up language model pretraining
Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy Liang, Quoc V. Le, Tengyu Ma, and Adams Wei Yu. 2023 · 2023
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Ties-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin A. Raffel, and Mohit Bansal. 2023 · 2023
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li. 2023 · 2023
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Investigating the catastrophic forgetting in multimodal large language models
Yuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Qing Qu, Yong Jae Lee, and Yi Ma. 2023 · 2023
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CITB: A benchmark for continual instruction tuning
Zihan Zhang, Meng Fang, Ling Chen, and Mohammad-Reza Namazi-Rad. 2023 · 2023
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Lora learns less and forgets less
Dan Biderman, Jose Gonzalez Ortiz, Jacob Portes, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, et al. 2024 · 2024
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Rethinking LLM language adaptation: A case study on chinese mixtral
Yiming Cui and Xin Yao. 2024 · 2024
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Flashattention-2: Faster attention with better parallelism and work partitioning
Tri Dao. 2024 · 2024
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Arcee’s mergekit: A toolkit for merging large language models
Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, Luke Meyers, Vlad Karpukhin, Brian Benedict, Mark McQuade, and Jacob Solawetz. 2024 · 2024
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Olmo: Accelerating the science of language models
Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, and Hannaneh Hajishirzi. 2024 · 2024
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Mitigating catastrophic forgetting in large language models with self-synthesized rehearsal
Jianheng Huang, Leyang Cui, Ante Wang, Chengyi Yang, Xinting Liao, Linfeng Song, Junfeng Yao, and Jinsong Su. 2024 · 2024
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Simple and scalable strategies to continually pre-train large language models
Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Anthony, Timothée Lesort, Eugene Belilovsky, and Irina Rish. 2024 · 2024
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NEFTune: Noisy embeddings improve instruction finetuning
Neel Jain, Ping yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2024 · 2024
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Model stock: All we need is just a few fine-tuned models
Dong-Hwan Jang, Sangdoo Yun, and Dongyoon Han. 2024 · 2024
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Instruction-tuned language models are better knowledge learners
Zhengbao Jiang, Zhiqing Sun, Weijia Shi, Pedro Rodriguez, Chunting Zhou, Graham Neubig, Xi Victoria Lin, Wen-tau Yih, and Srinivasan Iyer. 2024 · 2024
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Examining forgetting in continual pre-training of aligned large language models
Chen-An Li and Hung-Yi Lee. 2024 · 2024
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Mitigating the alignment tax of rlhf
Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, and Tong Zhang. 2024 · 2024
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Fineweb-edu
Anton Lozhkov, Loubna Ben Allal, Leandro von Werra, and Thomas Wolf. 2024 · 2024
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Orca-math: Unlocking the potential of slms in grade school math
Arindam Mitra, Hamed Khanpour, Corby Rosset, and Ahmed Awadallah. 2024 · 2024
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CulturaX: A cleaned, enormous, and multilingual dataset for large language models in 167 languages
Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, Hieu Man, Nghia Trung Ngo, Franck Dernoncourt, Ryan A. Rossi, and Thien Huu Nguyen. 2024 · 2024
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Continual learning of large language models: A comprehensive survey
Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang, and Hao Wang. 2024 · 2024
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Metamath: Bootstrap your own mathematical questions for large language models
Longhui Yu, Weisen Jiang, Han Shi, Jincheng YU, Zhengying Liu, Yu Zhang, James Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. 2024 · 2024
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Llama beyond english: An empirical study on language capability transfer
Jun Zhao, Zhihao Zhang, Luhui Gao, Qi Zhang, Tao Gui, and Xuanjing Huang. 2024 · 2024
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