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Position embedding is a core component of current Large Language Models (LLMs).
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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 · 2018
Earlier work this paper cites.
Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani · 2018
Earlier work this paper cites.
An augmented transformer architecture for natural language generation tasks
Hailiang Li, YC Adele, Yang Liu, Du Tang, Zhibin Lei, and Wenye Li · 2019
Earlier work this paper cites.
Compressive transformers for long-range sequence modelling
Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, Chloe Hillier, and Timothy P Lillicrap · 2019
Earlier work this paper cites.
Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan · 2020
Earlier work this paper cites.
Rethinking positional encoding in language pre-training
Guolin Ke, Di He, and Tie-Yan Liu · 2020
Earlier work this paper cites.
Megatron-lm: Training multi-billion parameter language models using model parallelism, 2020
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2020
Earlier work this paper cites.
Shape: Shifted absolute position embedding for transformers
Shun Kiyono, Sosuke Kobayashi, Jun Suzuki, and Kentaro Inui · 2021
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
Earlier work this paper cites.
A length-extrapolatable transformer
Yutao Sun, Li Dong, Barun Patra, Shuming Ma, Shaohan Huang, Alon Benhaim, Vishrav Chaudhary, Xia Song, and Furu Wei · 2022
Earlier work this paper cites.
Scaling laws vs model architectures: How does inductive bias influence scaling?
Yi Tay, Mostafa Dehghani, Samira Abnar, Hyung Won Chung, William Fedus, Jinfeng Rao, Sharan Narang, Vinh Q Tran, Dani Yogatama, and Donald Metzler · 2022
Earlier work this paper cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Earlier work this paper cites.
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
Cited alongside, same era.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al · 2023
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
bloc97 · 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
Cited alongside, same era.
Dissecting transformer length extrapolation via the lens of receptive field analysis
Ta-Chung Chi, Ting-Han Fan, Alexander Rudnicky, and Peter Ramadge · 2023
Ring attention with blockwise transformers for near-infinite context
Hao Liu, Matei Zaharia, and Pieter Abbeel · 2023
Later among the works it cites.
Rwkv: Reinventing rnns for the transformer era
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, et al · 2023
Later among the works it cites.
Baichuan 2: Open large-scale language models
Aiyuan Yang, Bin Xiao, Bingning Wang, Borong Zhang, Ce Bian, Chao Yin, Chenxu Lv, Da Pan, Dian Wang, Dong Yan, et al · 2023
Later among the works it cites.
Rotary position embedding for vision transformer
Byeongho Heo, Song Park, Dongyoon Han, and Sangdoo Yun · 2024
Closest in time.
Kvquant: Towards 10 million context length llm inference with kv cache quantization
Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W Mahoney, Yakun Sophia Shao, Kurt Keutzer, and Amir Gholami · 2024
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Cited alongside, same era.
Redpajama: An open source recipe to reproduce llama training dataset, April 2023
Together Computer · 2023
Cited alongside, same era.
Dynamically scaled rope further increases performance of long context llama with zero fine-tuning
emozilla · 2023
Cited alongside, same era.
Needle in a haystack - pressure testing llms
Kamradt G · 2023
Cited alongside, same era.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
Cited alongside, same era.
Lm-infinite: Simple on-the-fly length generalization for large language models
Chi Han, Qifan Wang, Wenhan Xiong, Yu Chen, Heng Ji, and Sinong Wang · 2023
Cited alongside, same era.
Impact of code language models on automated program repair
Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan · 2023
Cited alongside, same era.
How long can open-source llms truly promise on context length?, June 2023
Dacheng Li*, Rulin Shao*, Anze Xie, Ying Sheng, Lianmin Zheng, Joseph E. Gonzalez, Ion Stoica, Xuezhe Ma, and Hao Zhang · 2023
Cited alongside, same era.
Closest in time.
Can perplexity reflect large language model’s ability in long text understanding?
Yutong Hu, Quzhe Huang, Mingxu Tao, Chen Zhang, and Yansong Feng · 2024
Closest in time.
The impact of positional encoding on length generalization in transformers
Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Payel Das, and Siva Reddy · 2024
Closest in time.
Random-access infinite context length for transformers
Amirkeivan Mohtashami and Martin Jaggi · 2024
Closest in time.
Hierarchically gated recurrent neural network for sequence modeling
Zhen Qin, Songlin Yang, and Yiran Zhong · 2024
Closest in time.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu · 2024
Closest in time.
Focused transformer: Contrastive training for context scaling
Szymon Tworkowski, Konrad Staniszewski, Mikołaj Pacek, Yuhuai Wu, Henryk Michalewski, and Piotr Miłoś · 2024
Closest in time.
Yi: Open foundation models by 01. ai
Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, et al · 2024
Closest in time.