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Pretraining data selection has the potential to improve language model pretraining efficiency by utilizing higher-quality data from massive web data corpora.
Residuals and influence in regression
Sanford Weisberg and R Dennis Cook · 1982
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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The LAMBADA dataset: Word prediction requiring a broad discourse context
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández · 2016
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
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Crowdsourcing multiple choice science questions
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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
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Can a suit of armor conduct electricity? A new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement
Wouter Kool, Herke van Hoof, and Max Welling · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, Jeffrey Dean, and Sanjay Ghemawat · 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
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PIQA: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan LeBras, Jianfeng Gao, and Yejin Choi · 2020
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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
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ELECTRA: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning · 2020
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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
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LogiQA: A challenge dataset for machine reading comprehension with logical reasoning
Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2020
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CCNet: Extracting high quality monolingual datasets from web crawl data
Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave · 2020
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MC-BERT: Efficient language pre-training via a meta controller
Zhenhui Xu, Linyuan Gong, Guolin Ke, Di He, Shuxin Zheng, Liwei Wang, Jiang Bian, and Tie-Yan Liu · 2020
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The Pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2021
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COCO-LM: correcting and contrasting text sequences for language model pretraining
Yu Meng, Chenyan Xiong, Payal Bajaj, Saurabh Tiwary, Paul Bennett, Jiawei Han, and Xia Song · 2021
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Scaling language models: Methods, analysis & insights from training Gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al · 2021
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Accelerating deep learning with dynamic data pruning
Ravi S Raju, Kyle Daruwalla, and Mikko Lipasti · 2021
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METRO: Efficient denoising pretraining of large scale autoencoding language models with model generated signals
Payal Bajaj, Chenyan Xiong, Guolin Ke, Xiaodong Liu, Di He, Saurabh Tiwary, Tie-Yan Liu, Paul Bennett, Xia Song, and Jianfeng Gao · 2022
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PaLM: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, and et al · 2022
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TRAK: Attributing model behavior at scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry · 2023
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The RefinedWeb dataset for Falcon LLM: Outperforming curated corpora with web data only
Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Hamza Alobeidli, Alessandro Cappelli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay · 2023
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Self-influence guided data reweighting for language model pre-training
Megh Thakkar, Tolga Bolukbasi, Sriram Ganapathy, Shikhar Vashishth, Sarath Chandar, and Partha Talukdar · 2023
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D4: Improving LLM pretraining via document de-duplication and diversification
Kushal Tirumala, Daniel Simig, Armen Aghajanyan, and Ari Morcos · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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GLaM: Efficient scaling of language models with mixture-of-experts
Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P. Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen S. Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, and Claire Cui · 2022
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An empirical analysis of compute-optimal large language model training
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katherine Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Oriol Vinyals, Jack W. Rae, and Laurent Sifre · 2022
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Datamodels: Predicting predictions from training data
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, and Aleksander Madry · 2022
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Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini · 2022
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Pretraining text encoders with adversarial mixture of training signal generators
Yu Meng, Chenyan Xiong, Payal Bajaj, Saurabh Tiwary, Paul N. Bennett, Jiawei Han, and Xia Song · 2022
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ClueWeb22: 10 billion web documents with rich information
Arnold Overwijk, Chenyan Xiong, and Jamie Callan · 2022
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Scaling up influence functions
Andrea Schioppa, Polina Zablotskaia, David Vilar, and Artem Sokolov · 2022
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Data selection for language models via importance resampling
Sang Michael Xie, Shibani Santurkar, Tengyu Ma, and Percy Liang · 2023
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A survey on data selection for language models
Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, et al · 2024
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei · 2024
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DsDm: Model-aware dataset selection with datamodels
Logan Engstrom, Axel Feldmann, and Aleksander Madry · 2024
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Scaling laws for data filtering–data curation cannot be compute agnostic
Sachin Goyal, Pratyush Maini, Zachary C Lipton, Aditi Raghunathan, and J Zico Kolter · 2024
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Scaling laws and compute-optimal training beyond fixed training durations
Alexander Hägele, Elie Bakouch, Atli Kosson, Loubna Ben allal, Leandro Von Werra, and Martin Jaggi · 2024
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MiniCPM: Unveiling the potential of small language models with scalable training strategies
Shengding Hu, Yuge Tu, Xu Han, Chaoqun He, Ganqu Cui, Xiang Long, Zhi Zheng, Yewei Fang, Yuxiang Huang, Weilin Zhao, et al · 2024
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DataComp-LM: In search of the next generation of training sets for language models
Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, et al · 2024
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Rho-1: Not all tokens are what you need
Zhenghao Lin, Zhibin Gou, Yeyun Gong, Xiao Liu, Yelong Shen, Ruochen Xu, Chen Lin, Yujiu Yang, Jian Jiao, Nan Duan, et al · 2024
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A pretrainer’s guide to training data: Measuring the effects of data age, domain coverage, quality, & toxicity
Shayne Longpre, Gregory Yauney, Emily Reif, Katherine Lee, Adam Roberts, Barret Zoph, Denny Zhou, Jason Wei, Kevin Robinson, David Mimno, and Daphne Ippolito · 2024
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Rephrasing the web: A recipe for compute and data-efficient language modeling
Pratyush Maini, Skyler Seto, Richard Bai, David Grangier, Yizhe Zhang, and Navdeep Jaitly · 2024
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The FineWeb datasets: Decanting the web for the finest text data at scale
Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, and Thomas Wolf · 2024
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InfoBatch: Lossless training speed up by unbiased dynamic data pruning
Ziheng Qin, Kai Wang, Zangwei Zheng, Jianyang Gu, Xiangyu Peng, Daquan Zhou, Lei Shang, Baigui Sun, Xuansong Xie, Yang You, et al · 2024
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How to train data-efficient LLMs
Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni, Lichan Hong, Ed H Chi, James Caverlee, Julian McAuley, and Derek Zhiyuan Cheng · 2024
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Dolma: an open corpus of three trillion tokens for language model pretraining research
Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk, David Atkinson, Russell Authur, Ben Bogin, Khyathi Chandu, Jennifer Dumas, Yanai Elazar, Valentin Hofmann, Ananya Jha, Sachin Kumar, Li Lucy, Xinxi Lyu, Nathan Lambert, Ian Magnusson, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell, Nishant Subramani, Oyvind Tafjord, Evan Walsh, Luke Zettlemoyer, Noah Smith, Hannaneh Hajishirzi, Iz Beltagy, Dirk Groeneveld, Jesse Dodge, and Kyle Lo · 2024
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QuRating: Selecting high-quality data for training language models
Alexander Wettig, Aatmik Gupta, Saumya Malik, and Danqi Chen · 2024
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LESS: Selecting influential data for targeted instruction tuning
Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, and Danqi Chen · 2024
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