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Large language models (LLMs) have recently demonstrated remarkable performance across diverse language tasks.
Optimal brain damage
Yann LeCun, John Denker, and Sara Solla · 1989
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Using the eigenvalue relaxation for binary least-squares estimation problems
Stéphane Chrétien and Franck Corset · 2009
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Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang · 2013
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer · 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
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A semidefinite programming method for integer convex quadratic minimization
Jaehyun Park and Stephen Boyd · 2018
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang · 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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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov · 2019
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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 · 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 Le Bras, Jianfeng Gao, and Yejin Choi · 2020
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Up or down? adaptive rounding for post-training quantization
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort · 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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8-bit optimizers via block-wise quantization
Tim Dettmers, Mike Lewis, Sam Shleifer, and Luke Zettlemoyer · 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
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Accurate post training quantization with small calibration sets
Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, and Daniel Soudry · 2021
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Brecq: Pushing the limit of post-training quantization by block reconstruction
Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu · 2021
Cited alongside, same era.
Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
Cited alongside, same era.
Optimal brain compression: A framework for accurate post-training quantization and pruning
Elias Frantar and Dan Alistarh · 2022
Cited alongside, same era.
Gptq: Accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
Cited alongside, same era.
Outlier suppression: Pushing the limit of low-bit transformer language models
Xiuying Wei, Yunchen Zhang, Xiangguo Zhang, Ruihao Gong, Shanghang Zhang, Qi Zhang, Fengwei Yu, and Xianglong Liu · 2022
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, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
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Xiuying Wei, Yunchen Zhang, Yuhang Li, Xiangguo Zhang, Ruihao Gong, Jinyang Guo, and Xianglong Liu · 2023
Later among the works it cites.
Smoothquant: Accurate and efficient post-training quantization for large language models
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han · 2023
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Framequant: Flexible low-bit quantization for transformers
Harshavardhan Adepu, Zhanpeng Zeng, Li Zhang, and Vikas Singh · 2024
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Cited alongside, same era.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
Cited alongside, same era.
Quantease: Optimization-based quantization for language models
Kayhan Behdin, Ayan Acharya, Aman Gupta, Qingquan Song, Siyu Zhu, Sathiya Keerthi, and Rahul Mazumder · 2023
Cited alongside, same era.
Yuji Chai, John Gkountouras, Glenn G Ko, David Brooks, and Gu-Yeon Wei · 2023
Cited alongside, same era.
The case for 4-bit precision: k-bit inference scaling laws
Tim Dettmers and Luke Zettlemoyer · 2023
Cited alongside, same era.
Spqr: A sparse-quantized representation for near-lossless llm weight compression
Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan Alistarh · 2023
Cited alongside, same era.
Gemini: A family of highly capable multimodal models
Gemini Team Google · 2023
Cited alongside, same era.
Owq: Lessons learned from activation outliers for weight quantization in large language models
Changhun Lee, Jungyu Jin, Taesu Kim, Hyungjun Kim, and Eunhyeok Park · 2023
Cited alongside, same era.
Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman · 2024
Closest in time.
Quip: 2-bit quantization of large language models with guarantees
Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, and Christopher M De Sa · 2024
Closest in time.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2024
Closest in time.
Extreme compression of large language models via additive quantization
Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, and Dan Alistarh · 2024
Closest in time.
Gemma 2: Improving open language models at a practical size
Gemma-Team · 2024
Closest in time.
Compressing large language models by joint sparsification and quantization
Jinyang Guo, Jianyu Wu, Zining Wang, Jiaheng Liu, Ge Yang, Yifu Ding, Ruihao Gong, Haotong Qin, and Xianglong Liu · 2024
Closest in time.
Squeezellm: Dense-and-sparse quantization
Sehoon Kim, Coleman Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W. Mahoney, and Kurt Keutzer · 2024
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Spinquant: LLM quantization with learned rotations
Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran, Dhruv Choudhary, Raghuraman Krishnamoorthi, Vikas Chandra, Yuandong Tian, and Tijmen Blankevoort · 2024
Closest in time.
Affinequant: Affine transformation quantization for large language models
Yuexiao Ma, Huixia Li, Xiawu Zheng, Feng Ling, Xuefeng Xiao, Rui Wang, Shilei Wen, Fei Chao, and Rongrong Ji · 2024
Closest in time.
Gemma: Open models based on gemini research and technology
Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Léonard Hussenot, Aakanksha Chowdhery, Adam Roberts, Aditya Barua, Alex Botev, Alex Castro-Ros, Ambrose Slone, Amélie Héliou, Andrea Tacchetti, Anna Bulanova, Antonia Paterson, Beth Tsai, Bobak Shahriari, Charline Le Lan, Christopher A. Choquette-Choo, Clément Crepy, Daniel Cer, Daphne Ippolito, David Reid, Elena Buchatskaya, Eric Ni, Eric Noland, Geng Yan, George Tucker, George-Cristian Muraru, Grigory Rozhdestvenskiy, Henryk Michalewski, Ian Tenney, Ivan Grishchenko, Jacob Austin, James Keeling, Jane Labanowski, Jean-Baptiste Lespiau, Jeff Stanway, Jenny Brennan, Jeremy Chen, Johan Ferret, Justin Chiu, and et al · 2024
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Any-precision LLM: low-cost deployment of multiple, different-sized llms
Yeonhong Park, Jake Hyun, SangLyul Cho, Bonggeun Sim, and Jae W. Lee · 2024
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Quip#: Even better LLM quantization with hadamard incoherence and lattice codebooks
Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, and Christopher De Sa · 2024
Closest in time.
LQER: low-rank quantization error reconstruction for llms
Cheng Zhang, Jianyi Cheng, George Anthony Constantinides, and Yiren Zhao · 2024
Closest in time.