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Large language models have demonstrated outstanding performance on a wide range of tasks such as question answering and code generation.
Derivatives of regular expressions
Janusz A Brzozowski. 1964 · 1964
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Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. In Proc. of EMNLP
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Language models are unsupervised multitask learners
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Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
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The Pile: An 800GB Dataset of Diverse Text for Language Modeling
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Transformers: State-of-the-Art Natural Language Processing. In Proc. of EMNLP
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Training Verifiers to Solve Math Word Problems
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Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm. In CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama Japan, May 8-13, 2021, Extended Abstracts
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PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. In Proc. of EMNLP
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Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, and Benjamin Van Durme. 2021 · 2021
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PromptChainer: Chaining Large Language Model Prompts through Visual Programming. In CHI ’22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022 - 5 May 2022, Extended Abstracts
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langchain
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ChatGPT: Optimizing Language Models for Dialogue — openai.com
OpenAI. 2022 · 2022
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Generation
HuggingFace. 2023a
Cited in the paper.
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023 · 2023
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