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Large Language Models (LLMs) demonstrate remarkable emergent abilities across various tasks, yet fall short of complex reasoning and planning tasks.
A formal basis for the heuristic determination of minimum cost paths
Peter E Hart, Nils J Nilsson, and Bertram Raphael. 1968 · 1968
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Breadth-first search
Alan Bundy and Lincoln Wallen. 1984 · 1984
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Performance analysis of fcfs and improved fcfs scheduling algorithms for dynamic real-time computer systems
Wei Zhao and John A Stankovic. 1989 · 1989
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Bandit based monte-carlo planning
Levente Kocsis and Csaba Szepesvári. 2006 · 2006
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Findings of the 2014 workshop on statistical machine translation
Ondřej Bojar, Christian Buck, Christian Federmann, Barry Haddow, Philipp Koehn, Johannes Leveling, Christof Monz, Pavel Pecina, Matt Post, Herve Saint-Amand, Radu Soricut, Lucia Specia, and Aleš Tamchyna. 2014 · 2014
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Comparison analysis of cpu scheduling: Fcfs, sjf and round robin
Andysah Putera Utama Siahaan. 2016 · 2016
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Mask-predict: Parallel decoding of conditional masked language models
Marjan Ghazvininejad, Omer Levy, Yinhan Liu, and Luke Zettlemoyer. 2019 · 2019
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Introducing ChatGPT
OpenAI. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 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 · 2023
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Qwen technical report
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023 · 2023
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Skipdecode: Autoregressive skip decoding with batching and caching for efficient llm inference
Luciano Del Corro, Allison Del Giorno, Sahaj Agarwal, Bin Yu, Ahmed Hassan Awadallah, and Subhabrata Mukherjee. 2023 · 2023
Cited alongside, same era.
Leveraging pre-trained large language models to construct and utilize world models for model-based task planning
Lin Guan, Karthik Valmeekam, Sarath Sreedharan, and Subbarao Kambhampati. 2023 · 2023
Cited alongside, same era.
Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Hong, Zhen Wang, Daisy Wang, and Zhiting Hu. 2023 · 2023
Cited alongside, same era.
Rest: Retrieval-based speculative decoding
Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D Lee, and Di He. 2023 · 2023
Cited alongside, same era.
Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023
Cited alongside, same era.
LayerSkip: Enabling early exit inference and self-speculative decoding
Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Anas Mahmoud, Bilge Acun, Saurabh Agarwal, Ahmed Roman, Ahmed Aly, Beidi Chen, and Carole-Jean Wu. 2024 · 2024
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Rot: Enhancing large language models with reflection on search trees
Wenyang Hui, Yan Wang, Kewei Tu, and Chengyue Jiang. 2024 · 2024
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EAGLE: Speculative sampling requires rethinking feature uncertainty
Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. 2024 · 2024
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Look before you leap: Problem elaboration prompting improves mathematical reasoning in large language models
Haoran Liao, Jidong Tian, Shaohua Hu, Hao He, and Yaohui Jin. 2024 · 2024
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Ems-sd: Efficient multi-sample speculative decoding for accelerating large language models
Yunsheng Ni, Chuanjian Liu, Yehui Tang, Kai Han, and Yunhe Wang. 2024 · 2024
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Fast inference from transformers via speculative decoding
Yaniv Leviathan, Matan Kalman, and Yossi Matias. 2023 · 2023
Cited alongside, same era.
Online speculative decoding
Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Ion Stoica, Zhijie Deng, Alvin Cheung, and Hao Zhang. 2023 · 2023
Cited alongside, same era.
On the planning abilities of large language models-a critical investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati. 2023 · 2023
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023 · 2023
Cited alongside, same era.
Distillspec: Improving speculative decoding via knowledge distillation
Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, and Rishabh Agarwal. 2023 · 2023
Cited alongside, same era.
Speculative streaming: Fast llm inference without auxiliary models
Nikhil Bhendawade, Irina Belousova, Qichen Fu, Henry Mason, Mohammad Rastegari, and Mahyar Najibi. 2024 · 2024
Cited alongside, same era.
Medusa: Simple LLM inference acceleration framework with multiple decoding heads
Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, and Tri Dao. 2024 · 2024
Cited alongside, same era.
Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. 2024 · 2024
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A comparative study on reasoning patterns of openai’s o1 model
Siwei Wu, Zhongyuan Peng, Xinrun Du, Tuney Zheng, Minghao Liu, Jialong Wu, Jiachen Ma, Yizhi Li, Jian Yang, Wangchunshu Zhou, et al. 2024 · 2024
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Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding
Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, and Zhifang Sui. 2024 · 2024
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Monte carlo tree search boosts reasoning via iterative preference learning
Yuxi Xie, Anirudh Goyal, Wenyue Zheng, Min-Yen Kan, Timothy P Lillicrap, Kenji Kawaguchi, and Michael Shieh. 2024 · 2024
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Mirror: A multiple-perspective self-reflection method for knowledge-rich reasoning
Hanqi Yan, Qinglin Zhu, Xinyu Wang, Lin Gui, and Yulan He. 2024 · 2024
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Multi-candidate speculative decoding
Sen Yang, Shujian Huang, Xinyu Dai, and Jiajun Chen. 2024 · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024 · 2024
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STAR: Constraint LoRA with dynamic active learning for data-efficient fine-tuning of large language models
Linhai Zhang, Jialong Wu, Deyu Zhou, and Guoqiang Xu. 2024b · 2024
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