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Mixture-of-Experts (MoE) models are designed to enhance the efficiency of large language models (LLMs) without proportionally increasing the computational demands.
Mixture of experts: a literature survey
Saeed Masoudnia and Reza Ebrahimpour. 2014 · 2014
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Earlier work this paper cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2020
Earlier work this paper cites.
Pyhessian: Neural networks through the lens of the hessian. In 2020 IEEE international conference on big data (Big data) . IEEE, 581–590
Zhewei Yao, Amir Gholami, Kurt Keutzer, and Michael W Mahoney. 2020 · 2020
Earlier work this paper cites.
Zero-infinity: Breaking the gpu memory wall for extreme scale deep learning. In Proceedings of the international conference for high performance computing, networking, storage and analysis . 1–14
Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, and Yuxiong He. 2021 · 2021
Earlier work this paper cites.
{ \{ Zero-offload } \} : Democratizing { \{ billion-scale } \} model training. In 2021 USENIX Annual Technical Conference (USENIX ATC 21) . 551–564
Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, and Yuxiong He. 2021 · 2021
Earlier work this paper cites.
Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale. In SC22: International Conference for High Performance Computing, Networking, Storage and Analysis . IEEE, 1–15
Reza Yazdani Aminabadi, Samyam Rajbhandari, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Olatunji Ruwase, Shaden Smith, Minjia Zhang, Jeff Rasley, et al · 2022
Cited alongside, same era.
No language left behind: Scaling human-centered machine translation
Marta R Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, et al · 2022
Cited alongside, same era.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer. 2022 · 2022
Cited alongside, same era.
Half-Quadratic Quantization of Large Machine Learning Models
Hicham Badri and Appu Shaji. 2023 · 2023
Cited alongside, same era.
Fast inference of mixture-of-experts language models with offloading
Adaptive gating in mixture-of-experts based language models
Jiamin Li, Qiang Su, Yitao Yang, Yimin Jiang, Cong Wang, and Hong Xu. 2023 · 2023
Later among the works it cites.
Flexgen: High-throughput generative inference of large language models with a single gpu. In International Conference on Machine Learning . PMLR, 31094–31116
Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher Ré, Ion Stoica, and Ce Zhang. 2023 · 2023
Later among the works it cites.
Edgemoe: Fast on-device inference of moe-based large language models
Rongjie Yi, Liwei Guo, Shiyun Wei, Ao Zhou, Shangguang Wang, and Mengwei Xu. 2023 · 2023
Later among the works it cites.
Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
Closest in time.
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Artyom Eliseev and Denis Mazur. 2023 · 2023
Cited alongside, same era.
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
Ranggi Hwang, Jianyu Wei, Shijie Cao, Changho Hwang, Xiaohu Tang, Ting Cao, Mao Yang, and Minsoo Rhu. 2023 · 2023
Cited alongside, same era.
Squeezellm: Dense-and-sparse quantization
Sehoon Kim, Coleman Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W Mahoney, and Kurt Keutzer. 2023 · 2023
Cited alongside, same era.
Leyang Xue, Yao Fu, Zhan Lu, Luo Mai, and Mahesh Marina. 2024 · 2024
Closest in time.
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 Xing, et al · 2024
Closest in time.