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Despite the recent success of large language models (LLMs), LLMs are particularly challenging in long-sequence inference scenarios due to the quadratic computational complexity of the attention mechanism.
Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon · 2011
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Mathqa: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi · 2019
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Reducing transformer depth on demand with structured dropout
Angela Fan, Edouard Grave, and Armand Joulin · 2019
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Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu · 2019
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The (un) reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 2019
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Paul Michel, Omer Levy, and Graham Neubig · 2019
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Distilbert, a distilled version of bert: Smaller, faster, cheaper and lighter. arxiv 2019
Victor Sanh, L Debut, J Chaumond, and T Wolf · 2019
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al · 2020
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Power-bert: Accelerating bert inference via progressive word-vector elimination
Saurabh Goyal, Anamitra Roy Choudhury, Saurabh Raje, Venkatesan Chakaravarthy, Yogish Sabharwal, and Ashish Verma · 2020
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Length-adaptive transformer: Train once with length drop, use anytime with search
Gyuwan Kim and Kyunghyun Cho · 2020
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A review of winograd schema challenge datasets and approaches
Vid Kocijan, Thomas Lukasiewicz, Ernest Davis, Gary Marcus, and Leora Morgenstern · 2020
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How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer · 2020
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A framework for few-shot language model evaluation
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, et al · 2021
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Exploring the role of bert token representations to explain sentence probing results
Hosein Mohebbi, Ali Modarressi, and Mohammad Taher Pilehvar · 2021
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Dynamicvit: Efficient vision transformers with dynamic token sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh · 2021
Ziplm: Hardware-aware structured pruning of language models
Eldar Kurtic, Elias Frantar, and Dan Alistarh · 2023
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Bloom: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al · 2023
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Deja vu: Contextual sparsity for efficient llms at inference time
Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Re, et al · 2023
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Llm-pruner: On the structural pruning of large language models
Xinyin Ma, Gongfan Fang, and Xinchao Wang · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Winogrande: An adversarial winograd schema challenge at scale
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Spatten: Efficient sparse attention architecture with cascade token and head pruning
Hanrui Wang, Zhekai Zhang, and Song Han · 2021
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Gptq: Accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
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Learned token pruning for transformers
Sehoon Kim, Sheng Shen, David Thorsley, Amir Gholami, Woosuk Kwon, Joseph Hassoun, and Kurt Keutzer · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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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, et al · 2023
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Sparsegpt: Massive language models can be accurately pruned in one-shot
Elias Frantar and Dan Alistarh · 2023
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Xiaoxia Wu, Cheng Li, Reza Yazdani Aminabadi, Zhewei Yao, and Yuxiong He · 2023
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Focus on the core: Efficient attention via pruned token compression for document classification
Jungmin Yun, Mihyeon Kim, and Youngbin Kim · 2023
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Dynamic context pruning for efficient and interpretable autoregressive transformers
Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci, Aurelien Lucchi, and Thomas Hofmann · 2024
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Scissorhands: Exploiting the persistence of importance hypothesis for llm kv cache compression at test time
Zichang Liu, Aditya Desai, Fangshuo Liao, Weitao Wang, Victor Xie, Zhaozhuo Xu, Anastasios Kyrillidis, and Anshumali Shrivastava · 2024
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Llmlingua-2: Data distillation for efficient and faithful task-agnostic prompt compression
Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, et al · 2024
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Flexibly scaling large language models contexts through extensible tokenization
Ninglu Shao, Shitao Xiao, Zheng Liu, and Peitian Zhang · 2024
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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 · 2024
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