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Large language models show great potential in generating and optimizing code.
Statistical theory of extreme valuse and some practical applications
Emil Julius Gumbel · 1954
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Noisy parallel approximate decoding for conditional recurrent language model
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Mirostat: A neural text decoding algorithm that directly controls perplexity
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Unsupervised Translation of Programming Languages
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Incremental sampling without replacement for sequence models
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Implicit unlikelihood training: Improving neural text generation with reinforcement learning
Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models
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OpenAI · 2023
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Code Llama: Open Foundation Models for Code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve · 2023
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Competition-level code generation with AlphaCode
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Typical decoding for natural language generation
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Arithmetic sampling: parallel diverse decoding for large language models
Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Tachard Passos, and Sumit Sanghai · 2023
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Efficient guided generation for large language models
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Automated program repair in the era of large pre-trained language models
Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang · 2023
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A survey of controllable text generation using transformer-based pre-trained language models
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