Scaling LLM Inference with Optimized Sample Compute Allocation

Adaptive computation time for recurrent neural networks

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Alex Graves. 2016 · 2016

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Evaluating large language models trained on code

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Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 · 2021

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Language model cascades

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Competition-level code generation with alphacode

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Improving factuality and reasoning in language models through multiagent debate

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Enhancing large language models in coding through multi-perspective self-consistency

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Baizhou Huang, Shuai Lu, Weizhu Chen, Xiaojun Wan, and Nan Duan. 2023 · 2023

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LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Dongfu Jiang, Xiang Ren, and Bill Yuchen Lin. 2023 · 2023

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Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, Wen-tau Yih, Jason Weston, Jürgen Schmidhuber, and Xian Li. 2023 · 2023

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Large language monkeys: Scaling inference compute with repeated sampling

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Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini. 2024 · 2024

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Reflexion: Language agents with verbal reinforcement learning

Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024 · 2024

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Scaling llm test-time compute optimally can be more effective than scaling model parameters

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Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. 2024 · 2024

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Toward self-improvement of llms via imagination, searching, and criticizing

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Ye Tian, Baolin Peng, Linfeng Song, Lifeng Jin, Dian Yu, Haitao Mi, and Dong Yu. 2024 · 2024

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Llm can achieve self-regulation via hyperparameter aware generation

Siyin Wang, Shimin Li, Tianxiang Sun, Jinlan Fu, Qinyuan Cheng, Jiasheng Ye, Junjie Ye, Xipeng Qiu, Xuanjing Huang, Lun-Wei Ku, Andre Martins, and Vivek Srikumar. 2024 · 2024

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From decoding to meta-generation: Inference-time algorithms for large language models

Original