Fetching the paper…
Reading the bibliography…
Scaling the context size of large language models (LLMs) enables them to perform various new tasks, e.g., book summarization.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
Earlier work this paper cites.
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 · 2020
Earlier work this paper cites.
Attention is not only a weight: Analyzing transformers with vector norms
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui. 2020 · 2020
Earlier work this paper cites.
Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. 2022 · 2022
Earlier work this paper cites.
GPT3.int8(): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. 2022 · 2022
Earlier work this paper cites.
Transkimmer: Transformer learns to layer-wise skim
Yue Guan, Zhengyi Li, Jingwen Leng, Zhouhan Lin, and Minyi Guo. 2022 · 2022
Cited alongside, same era.
How much does attention actually attend? questioning the importance of attention in pretrained transformers
Michael Hassid, Hao Peng, Daniel Rotem, Jungo Kasai, Ivan Montero, Noah A Smith, and Roy Schwartz. 2022 · 2022
Cited alongside, same era.
Fine- and coarse-granularity hybrid self-attention for efficient BERT
Jing Zhao, Yifan Wang, Junwei Bao, Youzheng Wu, and Xiaodong He. 2022 · 2022
Cited alongside, same era.
GQA: Training generalized multi-query transformer models from multi-head checkpoints
Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebron, and Sumit Sanghai. 2023 · 2023
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, et al. 2023 · 2023
Booookscore: A systematic exploration of book-length summarization in the era of LLMs
Yapei Chang, Kyle Lo, Tanya Goyal, and Mohit Iyyer. 2024 · 2024
Closest in time.
A simple and effective l _ 2 l\_2 norm-based strategy for kv cache compression
Alessio Devoto, Yu Zhao, Simone Scardapane, and Pasquale Minervini. 2024 · 2024
Closest in time.
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. 2024 · 2024
Closest in time.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al. 2024 · 2024
Closest in time.
On the efficacy of eviction policy for key-value constrained generative language model inference
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
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. 2023 · 2023
Cited alongside, same era.
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 Re, Clark Barrett, Zhangyang Wang, and Beidi Chen. 2023 · 2023
Cited alongside, same era.
Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023 · 2023
Cited alongside, same era.
Rishabh Agarwal, Avi Singh, Lei M Zhang, Bernd Bohnet, Stephanie Chan, Ankesh Anand, Zaheer Abbas, Azade Nova, John D Co-Reyes, Eric Chu, et al. 2024 · 2024
Cited alongside, same era.
A simple and effective pruning approach for large language models
Mingjie Sun, Zhuang Liu, Anna Bair, and J Zico Kolter. 2024b
Cited in the paper.
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. 2023a
Cited in the paper.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, and et al. 2023b
Cited in the paper.
Siyu Ren and Kenny Q Zhu. 2024 · 2024
Closest in time.
Massive activations in large language models
Mingjie Sun, Xinlei Chen, J Zico Kolter, and Zhuang Liu. 2024a · 2024
Closest in time.
Efficient streaming language models with attention sinks
Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. 2024 · 2024
Closest in time.
Swe-agent: Agent-computer interfaces enable automated software engineering
John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. 2024 · 2024
Closest in time.