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Large language models (LLMs) such as GPT-4 have exhibited remarkable performance in a variety of tasks, but this strong performance often comes with the high expense of using paid API services.
Ensemble-based classifiers
Lior Rokach · 2010
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Training verifiers to solve math word problems
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
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Language models (mostly) know what they know, 2022
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Fast inference from transformers via speculative decoding
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Solving quantitative reasoning problems with language models
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Neeraj Varshney and Chitta Baral · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
BIG bench authors · 2023
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Self-refine: Iterative refinement with self-feedback
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Gpt-4 technical report, 2023
OpenAI · 2023
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Shima Imani, Liang Du, and Harsh Shrivastava · 2023
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