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Large language models (LLMs) have emerged as a cornerstone in real-world applications with lengthy streaming inputs (e.g., LLM-driven agents).
Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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End-to-end memory networks
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus · 2015
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Memory networks
Jason Weston, Sumit Chopra, and Antoine Bordes · 2015
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Key-value memory networks for directly reading documents
Alexander H. Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston · 2016
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Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc Viet Le, and Ruslan Salakhutdinov · 2019
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Explicit sparse transformer: Concentrated attention through explicit selection
Guangxiang Zhao, Junyang Lin, Zhiyuan Zhang, Xuancheng Ren, Qi Su, and Xu Sun · 2019
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ETC: encoding long and structured inputs in transformers
Joshua Ainslie, Santiago Ontañón, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, and Li Yang · 2020
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Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan · 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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Funnel-transformer: Filtering out sequential redundancy for efficient language processing
Zihang Dai, Guokun Lai, Yiming Yang, and Quoc Le · 2020
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Power-bert: Accelerating BERT inference via progressive word-vector elimination
Saurabh Goyal, Anamitra Roy Choudhury, Saurabh Raje, Venkatesan T. Chakaravarthy, Yogish Sabharwal, and Ashish Verma · 2020
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Transformers are RNNs: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 2020
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Reformer: The efficient transformer
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya · 2020
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Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela · 2020
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Compressive transformers for long-range sequence modelling
Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, and Timothy P. Lillicrap · 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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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontañón, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, and Amr Ahmed · 2020
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, and et al · 2021
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Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, and Jun Zhu · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman · 2021
Cited alongside, same era.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Bo Wen, and Yunfeng Liu · 2021
Cited alongside, same era.
Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe · 2022
Cited alongside, same era.
Ntk-aware scaled rope allows llama models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation
LocalLLaMA · 2023
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MTEB: massive text embedding benchmark
Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers · 2023
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OpenAI · 2023
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Generative agents: Interactive simulacra of human behavior
Joon Sung Park, Joseph C. O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein · 2023
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Yarn: Efficient context window extension of large language models
Bowen Peng, Jeffrey Quesnelle, Honglu Fan, and Enrico Shippole · 2023
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Train short, test long: Attention with linear biases enables input length extrapolation
Ofir Press, Noah A. Smith, and Mike Lewis · 2022
Cited alongside, same era.
Memorizing transformers
Yuhuai Wu, Markus Norman Rabe, DeLesley Hutchins, and Christian Szegedy · 2022
Cited alongside, same era.
Longbench: A bilingual, multitask benchmark for long context understanding
Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, and Juanzi Li · 2023
Cited alongside, same era.
Unlimiformer: Long-range transformers with unlimited length input
Amanda Bertsch, Uri Alon, Graham Neubig, and Matthew R. Gormley · 2023
Cited alongside, same era.
Vicuna: An open-source chatbot impressing GPT-4 with 90% ChatGPT quality, March 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
Cited alongside, same era.
Flashattention-2: Faster attention with better parallelism and work partitioning
Tri Dao · 2023
Cited alongside, same era.
A survey on long text modeling with transformers
Zican Dong, Tianyi Tang, Junyi Li, and Wayne Xin Zhao · 2023
Cited alongside, same era.
Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, and Pete Florence · 2023
Cited alongside, same era.
Chen Qian, Xin Cong, Cheng Yang, Weize Chen, Yusheng Su, Juyuan Xu, Zhiyuan Liu, and Maosong Sun · 2023
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Rectified rotary position embeddings, 2023
Jianlin Su · 2023
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A length-extrapolatable transformer
Yutao Sun, Li Dong, Barun Patra, Shuming Ma, Shaohan Huang, Alon Benhaim, Vishrav Chaudhary, Xia Song, and Furu Wei · 2023
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Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler · 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
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Focused transformer: Contrastive training for context scaling
Szymon Tworkowski, Konrad Staniszewski, Mikolaj Pacek, Yuhuai Wu, Henryk Michalewski, and Piotr Milos · 2023
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Efficient streaming language models with attention sinks
Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis · 2023
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Effective long-context scaling of foundation models
Wenhan Xiong, Jingyu Liu, Igor Molybog, Hejia Zhang, Prajjwal Bhargava, Rui Hou, Louis Martin, Rashi Rungta, Karthik Abinav Sankararaman, Barlas Oguz, Madian Khabsa, Han Fang, Yashar Mehdad, Sharan Narang, Kshitiz Malik, Angela Fan, Shruti Bhosale, Sergey Edunov, Mike Lewis, Sinong Wang, and Hao Ma · 2023
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Retrieval meets long context large language models
Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, and Bryan Catanzaro · 2023
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Length extrapolation of transformers: A survey from the perspective of position encoding
Liang Zhao, Xiaocheng Feng, Xiachong Feng, Bing Qin, and Ting Liu · 2023
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Training-free long-context scaling of large language models
Chenxin An, Fei Huang, Jun Zhang, Shansan Gong, Xipeng Qiu, Chang Zhou, and Lingpeng Kong · 2024
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Longrope: Extending LLM context window beyond 2 million tokens
Yiran Ding, Li Lyna Zhang, Chengruidong Zhang, Yuanyuan Xu, Ning Shang, Jiahang Xu, Fan Yang, and Mao Yang · 2024
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LLM maybe longlm: Self-extend LLM context window without tuning
Hongye Jin, Xiaotian Han, Jingfeng Yang, Zhimeng Jiang, Zirui Liu, Chia-Yuan Chang, Huiyuan Chen, and Xia Hu · 2024
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Snapkv: Llm knows what you are looking for before generation
Yuhong Li, Yingbing Huang, Bowen Yang, Bharat Venkitesh, Acyr Locatelli, Hanchen Ye, Tianle Cai, Patrick Lewis, and Deming Chen · 2024
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Introducing meta llama 3: The most capable openly available llm to date
Meta · 2024
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Leave no context behind: Efficient infinite context transformers with infini-attention
Tsendsuren Munkhdalai, Manaal Faruqui, and Siddharth Gopal · 2024
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