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Large language models are often used to answer queries grounded in large text corpora (e.g.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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chrf: character n-gram f-score for automatic mt evaluation
Maja Popović · 2015
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Sequence-level knowledge distillation
Yoon Kim and Alexander M Rush · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
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Fast transformer decoding: One write-head is all you need
Noam Shazeer · 2019
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
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Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al · 2020
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A dataset of information-seeking questions and answers anchored in research papers
Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A Smith, and Matt Gardner · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg · 2021
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Can foundation models help us achieve perfect secrecy?
Simran Arora and Christopher Ré · 2022
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Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
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A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, et al · 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, Weizhu Chen, et al · 2022
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Learning by distilling context
Charlie Snell, Dan Klein, and Ruiqi Zhong · 2022
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Gqa: Training generalized multi-query transformer models from multi-head checkpoints
Joshua Ainslie, James Lee-Thorp, Michiel De Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai · 2023
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Zoology: Measuring and improving recall in efficient language models, 2023
Simran Arora, Sabri Eyuboglu, Aman Timalsina, Isys Johnson, Michael Poli, James Zou, Atri Rudra, and Christopher Ré · 2023
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Adapting language models to compress contexts
Alexis Chevalier, Alexander Wettig, Anirudh Ajith, and Danqi Chen · 2023
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Model tells you what to discard: Adaptive kv cache compression for llms
Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, and Jianfeng Gao · 2023
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In-context autoencoder for context compression in a large language model
Tao Ge, Jing Hu, Lei Wang, Xun Wang, Si-Qing Chen, and Furu Wei · 2023
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Mixture of cluster-conditional lora experts for vision-language instruction tuning
Yunhao Gou, Zhili Liu, Kai Chen, Lanqing Hong, Hang Xu, Aoxue Li, Dit-Yan Yeung, James T Kwok, and Yu Zhang · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Legalbench: A collaboratively built benchmark for measuring legal reasoning in large language models
Neel Guha, Julian Nyarko, Daniel Ho, Christopher Ré, Adam Chilton, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel Rockmore, Diego Zambrano, et al · 2023
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Lorahub: Efficient cross-task generalization via dynamic lora composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, and Min Lin · 2023
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Financebench: A new benchmark for financial question answering
Pranab Islam, Anand Kannappan, Douwe Kiela, Rebecca Qian, Nino Scherrer, and Bertie Vidgen · 2023
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Llmlingua: Compressing prompts for accelerated inference of large language models
Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu · 2023
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LLMLingua: Compressing prompts for accelerated inference of large language models
Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu · 2023
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Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica · 2023
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Yucheng Li · 2023
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Learning to compress prompts with gist tokens
Jesse Mu, Xiang Li, and Noah Goodman · 2023
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Memgpt: Towards llms as operating systems
Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G Patil, Ion Stoica, and Joseph E Gonzalez · 2023
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Dodo: Dynamic contextual compression for decoder-only lms
Guanghui Qin, Corby Rosset, Ethan C Chau, Nikhil Rao, and Benjamin Van Durme · 2023
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Sparq attention: Bandwidth-efficient llm inference
Luka Ribar, Ivan Chelombiev, Luke Hudlass-Galley, Charlie Blake, Carlo Luschi, and Douglas Orr · 2023
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A benchmark for learning to translate a new language from one grammar book
Garrett Tanzer, Mirac Suzgun, Eline Visser, Dan Jurafsky, and Luke Melas-Kyriazi · 2023
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Plug-and-play document modules for pre-trained models
Chaojun Xiao, Zhengyan Zhang, Xu Han, Chi-Min Chan, Yankai Lin, Zhiyuan Liu, Xiangyang Li, Zhonghua Li, Zhao Cao, and Maosong Sun · 2023
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Plug-and-play knowledge injection for pre-trained language models
Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Huadong Wang, Deming Ye, Chaojun Xiao, Xu Han, Zhiyuan Liu, Peng Li, Maosong Sun, et al · 2023
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H2o: Heavy-hitter oracle for efficient generative inference of large language models
Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, et al · 2023
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Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J Hewett, Mojan Javaheripi, Piero Kauffmann, et al · 2024
Cited alongside, same era.
Longhealth: A question answering benchmark with long clinical documents
Lisa Adams, Felix Busch, Tianyu Han, Jean-Baptiste Excoffier, Matthieu Ortala, Alexander Löser, Hugo JWL Aerts, Jakob Nikolas Kather, Daniel Truhn, and Keno Bressem · 2024
Cited alongside, same era.
The Claude 3 Model Family: Opus, Sonnet, Haiku
Anthropic · 2024
Cited alongside, same era.
Simple linear attention language models balance the recall-throughput tradeoff
Configurable foundation models: Building llms from a modular perspective
Chaojun Xiao, Zhengyan Zhang, Chenyang Song, Dazhi Jiang, Feng Yao, Xu Han, Xiaozhi Wang, Shuo Wang, Yufei Huang, Guanyu Lin, et al · 2024
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Duoattention: Efficient long-context llm inference with retrieval and streaming heads
Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, Junxian Guo, Shang Yang, Haotian Tang, Yao Fu, and Song Han · 2024
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Efficient streaming language models with attention sinks, 2024
Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis · 2024
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Prateek Yadav, Colin Raffel, Mohammed Muqeeth, Lucas Caccia, Haokun Liu, Tianlong Chen, Mohit Bansal, Leshem Choshen, and Alessandro Sordoni · 2024
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Simran Arora, Sabri Eyuboglu, Michael Zhang, Aman Timalsina, Silas Alberti, Dylan Zinsley, James Zou, Atri Rudra, and Christopher Ré · 2024
Cited alongside, same era.
xlstm: Extended long short-term memory
Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter, and Sepp Hochreiter · 2024
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Titans: Learning to memorize at test time
Ali Behrouz, Peilin Zhong, and Vahab Mirrokni · 2024
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Aman Bhargava, Cameron Witkowski, Alexander Detkov, and Matt Thomson · 2024
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Palu: Compressing kv-cache with low-rank projection
Chi-Chih Chang, Wei-Cheng Lin, Chien-Yu Lin, Chong-Yan Chen, Yu-Fang Hu, Pei-Shuo Wang, Ning-Chi Huang, Luis Ceze, Mohamed S Abdelfattah, and Kai-Chiang Wu · 2024
Cited alongside, same era.
Punica: Multi-tenant lora serving
Lequn Chen, Zihao Ye, Yongji Wu, Danyang Zhuo, Luis Ceze, and Arvind Krishnamurthy · 2024
Cited alongside, same era.
Learning to compress prompt in natural language formats
Yu-Neng Chuang, Tianwei Xing, Chia-Yuan Chang, Zirui Liu, Xun Chen, and Xia Hu · 2024
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
Cited alongside, same era.
An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al · 2024
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Gated linear attention transformers with hardware-efficient training
Songlin Yang, Bailin Wang, Yikang Shen, Rameswar Panda, and Yoon Kim · 2024
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Parallelizing linear transformers with the delta rule over sequence length
Songlin Yang, Bailin Wang, Yu Zhang, Yikang Shen, and Yoon Kim · 2024
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Fla: A triton-based library for hardware-efficient implementations of linear attention mechanism, January 2024
Songlin Yang and Yu Zhang · 2024
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Long-context language modeling with parallel context encoding
Howard Yen · 2024
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Effectively compress kv heads for llm
Hao Yu, Zelan Yang, Shen Li, Yong Li, and Jianxin Wu · 2024
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Lolcats: On low-rank linearizing of large language models
Michael Zhang, Simran Arora, Rahul Chalamala, Alan Wu, Benjamin Spector, Aaryan Singhal, Krithik Ramesh, and Christopher Ré · 2024
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Qianchi Zhang, Hainan Zhang, Liang Pang, Hongwei Zheng, and Zhiming Zheng · 2024
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Lorc: Low-rank compression for llms kv cache with a progressive compression strategy
Rongzhi Zhang, Kuang Wang, Liyuan Liu, Shuohang Wang, Hao Cheng, Chao Zhang, and Yelong Shen · 2024
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Cam: Cache merging for memory-efficient llms inference
Yuxin Zhang, Yuxuan Du, Gen Luo, Yunshan Zhong, Zhenyu Zhang, Shiwei Liu, and Rongrong Ji · 2024
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Loraretriever: Input-aware lora retrieval and composition for mixed tasks in the wild
Ziyu Zhao, Leilei Gan, Guoyin Wang, Wangchunshu Zhou, Hongxia Yang, Kun Kuang, and Fei Wu · 2024
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Ziyu Zhao, Tao Shen, Didi Zhu, Zexi Li, Jing Su, Xuwu Wang, Kun Kuang, and Fei Wu · 2024
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Sglang: Efficient execution of structured language model programs
Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Livia Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E Gonzalez, et al · 2024
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Atlas: Learning to optimally memorize the context at test time
Ali Behrouz, Zeman Li, Praneeth Kacham, Majid Daliri, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, and Vahab Mirrokni · 2025
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Ali Behrouz, Meisam Razaviyayn, Peilin Zhong, and Vahab Mirrokni · 2025
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Nemotron-h: A family of accurate and efficient hybrid mamba-transformer models
Aaron Blakeman, Aarti Basant, Abhinav Khattar, Adithya Renduchintala, Akhiad Bercovich, Aleksander Ficek, Alexis Bjorlin, Ali Taghibakhshi, Amala Sanjay Deshmukh, Ameya Sunil Mahabaleshwarkar, et al · 2025
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Training plug-n-play knowledge modules with deep context distillation
Lucas Caccia, Alan Ansell, Edoardo Ponti, Ivan Vulic, and Alessandro Sordoni · 2025
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Kv-distill: Nearly lossless learnable context compression for llms
Vivek Chari, Guanghui Qin, and Benjamin Van Durme · 2025
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Niyama: Breaking the silos of llm inference serving
Kanishk Goel, Jayashree Mohan, Nipun Kwatra, Ravi Shreyas Anupindi, and Ramachandran Ramjee · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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Serving models, fast and slow: optimizing heterogeneous llm inferencing workloads at scale
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Tokasaurus: An llm inference engine for high-throughput workloads, June 2025
Jordan Juravsky, Ayush Chakravarthy, Ryan Ehrlich, Sabri Eyuboglu, Bradley Brown, Joseph Shetaye, Christopher Ré, and Azalia Mirhoseini · 2025
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Yuri Kuratov, Mikhail Arkhipov, Aydar Bulatov, and Mikhail Burtsev · 2025
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Minimax-01: Scaling foundation models with lightning attention
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Lift: Improving long context understanding of large language models through long input fine-tuning
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Minions: Cost-efficient collaboration between on-device and cloud language models
Avanika Narayan, Dan Biderman, Sabri Eyuboglu, Avner May, Scott Linderman, James Zou, and Christopher Re · 2025
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Can ai hold office hours?
Lisa Larrimore Ouellette, Amy Motomura, Jason Reinecke, and Jonathan S Masur · 2025
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Metasynth: Meta-prompting-driven agentic scaffolds for diverse synthetic data generation
Haris Riaz, Sourav Bhabesh, Vinayak Arannil, Miguel Ballesteros, and Graham Horwood · 2025
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Parametric retrieval augmented generation
Weihang Su, Yichen Tang, Qingyao Ai, Junxi Yan, Changyue Wang, Hongning Wang, Ziyi Ye, Yujia Zhou, and Yiqun Liu · 2025
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How to read a 10-k, 2011
U.S. Securities and Exchange Commission · 2025
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Parallelizing linear transformers with the delta rule over sequence length, 2025
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Flashinfer: Efficient and customizable attention engine for llm inference serving
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Tensor product attention is all you need
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A reasoning-focused legal retrieval benchmark
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Elitekv: Scalable kv cache compression via rope frequency selection and joint low-rank projection
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