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The growing popularity of Large Language Models has sparked interest in context compression for Large Language Models (LLMs).
HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning · 2018
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Simplifying neural machine translation with addition-subtraction twin-gated recurrent networks
Biao Zhang, Deyi Xiong, Jinsong Su, Qian Lin, and Huiji Zhang. a · 2018
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2020
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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 · 2020
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Making monolingual sentence embeddings multilingual using knowledge distillation
Nils Reimers and Iryna Gurevych. 2020 · 2020
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Reader-guided passage reranking for open-domain question answering
Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen · 2021
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Train short, test long: Attention with linear biases enables input length extrapolation
Ofir Press, Noah A Smith, and Mike Lewis. 2021 · 2021
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A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 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, and Weizhu Chen. 2022 · 2022
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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. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Earlier work this paper cites.
Aan+: Generalized average attention network for accelerating neural transformer
Biao Zhang, Deyi Xiong, Yubin Ge, Junfeng Yao, Hao Yue, and Jinsong Su. 2022 · 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 · 2023
Cited alongside, same era.
Unlimiformer: Long-range transformers with unlimited length input
Amanda Bertsch, Uri Alon, Graham Neubig, and Matthew R Gormley. 2023 · 2023
Cited alongside, same era.
Extending context window of large language models via positional interpolation
Shouyuan Chen, Sherman Wong, Liangjian Chen, and Yuandong Tian. 2023 · 2023
Cited alongside, same era.
Compressing context to enhance inference efficiency of large language models
Yucheng Li, Bo Dong, Frank Guerin, and Chenghua Lin. 2023 · 2023
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Lost in the middle: How language models use long contexts
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023 · 2023
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When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi · 2023
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Learning to compress prompts with gist tokens
Jesse Mu, Xiang Lisa Li, and Noah Goodman. 2023 · 2023
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Alexis Chevalier, Alexander Wettig, Anirudh Ajith, and Danqi Chen. 2023 · 2023
Cited alongside, same era.
Longnet: Scaling transformers to 1,000,000,000 tokens
Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, Nanning Zheng, and Furu Wei. 2023 · 2023
Cited alongside, same era.
In-context autoencoder for context compression in a large language model
Tao Ge, Jing Hu, Xun Wang, Si-Qing Chen, and Furu Wei. 2023 · 2023
Cited alongside, same era.
Raven: In-context learning with retrieval augmented encoder-decoder language models
Jie Huang, Wei Ping, Peng Xu, Mohammad Shoeybi, Kevin Chen-Chuan Chang, and Bryan Catanzaro. 2023 · 2023
Cited alongside, same era.
LLMLingua: Compressing prompts for accelerated inference of large language models
Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. a · 2023
Cited alongside, same era.
Longllmlingua: Accelerating and enhancing llms in long context scenarios via prompt compression
Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. 2023 · 2023
Cited alongside, same era.
“low-resource” text classification: A parameter-free classification method with compressors
Zhiying Jiang, Matthew Yang, Mikhail Tsirlin, Raphael Tang, Yiqin Dai, and Jimmy Lin. b · 2023
Cited alongside, same era.
Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2022a
Cited in the paper.
Erik Nijkamp, Tian Xie, Hiroaki Hayashi, Bo Pang, Congying Xia, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, et al. 2023 · 2023
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Gpt-4 technical report
OpenAI OpenAI. 2023 · 2023
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Yarn: Efficient context window extension of large language models
Bowen Peng, Jeffrey Quesnelle, Honglu Fan, and Enrico Shippole. 2023 · 2023
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C-pack: Packaged resources to advance general chinese embedding
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. 2023 · 2023
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Recomp: Improving retrieval-augmented lms with compression and selective augmentation
Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2023 · 2023
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MoEfication: Transformer feed-forward layers are mixtures of experts
Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. b · 2023
Later among the works it cites.