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Large language models typically generate tokens autoregressively, using each token as input for the next.
A. Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
M. Stern, N. Shazeer, and J. Uszkoreit, “Blockwise parallel decoding for deep autoregressive models,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Earlier work this paper cites.
2021
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
X. Jiang, Y. Dong, L. Wang, F. Zheng, Q. Shang, G. Li, Z. Jin, and W. Jiao, “Self-planning code generation with large language models,” ACM Transactions on Software Engineering and Methodology , 2023
2023
Cited alongside, same era.
L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. Xing et al. , “Judging llm-as-a-judge with mt-bench and chatbot arena,” Advances in Neural Information Processing Systems , vol. 36, pp. 46 595–46 623, 2023
2023
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Closest in time.
Z. He, Z. Zhong, T. Cai, J. Lee, and D. He, “Rest: Retrieval-based speculative decoding,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , 2024, pp. 1582–1595
2024
Closest in time.
Y. Zhou, K. Lyu, A. S. Rawat, A. K. Menon, A. Rostamizadeh, S. Kumar, J.-F. Kagy, and R. Agarwal, “Distillspec: Improving speculative decoding via knowledge distillation,” in The Twelfth International Conference on Learning Representations , 2024
2024
Closest in time.
2024
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X. Miao, G. Oliaro, Z. Zhang, X. Cheng, Z. Wang, Z. Zhang, R. Y. Y. Wong, A. Zhu, L. Yang, X. Shi et al. , “Specinfer: Accelerating large language model serving with tree-based speculative inference and verification,” in Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3 , 2024, pp. 932–949
2024
Cited alongside, same era.
2024
Cited alongside, same era.
Closest in time.
2024
Closest in time.
B. McDanel, “Refactorchat: A multi-turn dialogue dataset for code refactoring,” https://huggingface.co/datasets/BradMcDanel/refactorchat , 2024, accessed: October 2024
2024
Closest in time.