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In large language model (LLM) pretraining, data quality is believed to determine model quality.
Lower bounds on the maximum cross correlation of signals (corresp.)
Lloyd Welch · 2003
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Linguistic regularities in continuous space word representations
Tomáš Mikolov, Wen-tau Yih, and Geoffrey Zweig · 2013
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
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Probing classifiers: Promises, shortcomings, and advances
Yonatan Belinkov · 2016
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Linear algebraic structure of word senses, with applications to polysemy
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski · 2018
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Toxic comment classification challenge, 2018
Jigsaw and Conversation AI · 2018
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Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu · 2019
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Openwebtext corpus
Aaron Gokaslan and Vanya Cohen · 2019
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Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher · 2019
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Bert rediscovers the classical nlp pipeline
Ian Tenney, Dipanjan Das, and Ellie Pavlick · 2019
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith · 2020
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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
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Gedi: Generative discriminator guided sequence generation
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani · 2020
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Interpreting gpt: The logit lens
nostalgebraist · 2020
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Raiders of the lost kek: 3.5 years of augmented 4chan posts from the politically incorrect board
Antonis Papasavva, Savvas Zannettou, Emiliano De Cristofaro, Gianluca Stringhini, and Jeremy Blackburn · 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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Dexperts: Decoding-time controlled text generation with experts and anti-experts
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A Smith, and Yejin Choi · 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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Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in nlp
Timo Schick, Sahana Udupa, and Hinrich Schütze · 2021
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Process for adapting language models to society (palms) with values-targeted datasets
Irene Solaiman and Christy Dennison · 2021
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Disentangling transformer language models as superposed topic models
Jia Peng Lim and Hady Lauw · 2023
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Yong Lin, Lu Tan, Hangyu Lin, Zeming Zheng, Renjie Pi, Jipeng Zhang, Shizhe Diao, Haoxiang Wang, Han Zhao, Yuan Yao, et al · 2023
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Shayne Longpre, Gregory Yauney, Emily Reif, Katherine Lee, Adam Roberts, Barret Zoph, Denny Zhou, Jason Wei, Kevin Robinson, David Mimno, and Daphne Ippolito · 2023
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Adding instructions during pretraining: Effective way of controlling toxicity in language models
Shrimai Prabhumoye, Mostofa Patwary, Mohammad Shoeybi, and Bryan Catanzaro · 2023
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Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, et al · 2022
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ToxiGen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
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Contrastive decoding: Open-ended text generation as optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis · 2022
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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
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al · 2022
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Exploring the limits of domain-adaptive training for detoxifying large-scale language models
Boxin Wang, Wei Ping, Chaowei Xiao, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Bo Li, Anima Anandkumar, and Bryan Catanzaro · 2022
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Eliciting latent predictions from transformers with the tuned lens
Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt · 2023
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Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson · 2023
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Direct preference optimization: Your language model is secretly a reward model
Ryan Rafailov, Jason Li, Ehsan Imani, Xinyun Zhang, and Dawn Song · 2023
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Activation addition: Steering language models without optimization
Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan J Vazquez, Ulisse Mini, and Monte MacDiarmid · 2023
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Rolellm: Benchmarking, eliciting, and enhancing role-playing abilities of large language models
Zekun Moore Wang, Zhongyuan Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Man Zhang, et al · 2023
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Representation engineering: A top-down approach to ai transparency
Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al · 2023
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Toxicity of the Commons: Curating Open-Source Pre-Training Data
Catherine Arnett, Eliot Jones, Ivan P. Yamshchikov, and Pierre-Carl Langlais · 2024
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The accuracy paradox in rlhf: When better reward models don’t yield better language models
Yanjun Chen, Dawei Zhu, Yirong Sun, Xinghao Chen, Wei Zhang, and Xiaoyu Shen · 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, et al · 2024
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A mechanistic understanding of alignment algorithms: A case study on dpo and toxicity
Andrew Lee, Xiaoyan Bai, Itamar Pres, Martin Wattenberg, Jonathan K Kummerfeld, and Rada Mihalcea · 2024
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Amd-olmo: A series of 1b language models trained from scratch by amd on amd instinct™ mi250 gpus., October 2024
Jiang Liu, Jialian Wu, Prakamya Mishra, Zicheng Liu, Sudhanshu Ranjan, Pratik Prabhanjan Brahma, Yusheng Su, Gowtham Ramesh, Peng Sun, Zhe Li, Dong Li, Lu Tian, and Emad Barsoum · 2024
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Perspective — developers, 2024
PerspectiveAPI · 2024
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On linear representations and pretraining data frequency in language models
Jack Merullo, Noah A Smith, Sarah Wiegreffe, and Yanai Elazar · 2025
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