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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Multilingual CoNaLa Datset, train data ... Zhiruo Wang; Cuenca, Grace; Shuyan Zhou. - : Zenodo, 2022 BASE Show details 2 Multilingual CoNaLa Datset, train data ... Zhiruo Wang; Cuenca, Grace; Shuyan Zhou. - : Zenodo, 2022 BASE Show details 3 MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages ... Wang, Zhiruo; Cuenca, Grace; Zhou, Shuyan. - : arXiv, 2022 BASE Show details 4 A Systematic Evaluation of Large Language Models of Code ... Xu, Frank F.; Alon, Uri; Neubig, Graham; Hellendoorn, Vincent J.. - : arXiv, 2022 Abstract: Large language models (LMs) of code have recently shown tremendous promise in completing code and synthesizing code from natural language descriptions. However, the current state-of-the-art code LMs (e.g., Codex (Chen et al., 2021)) are not publicly available, leaving many questions about their model and data design decisions. We aim to fill in some of these blanks through a systematic evaluation of the largest existing models: Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot, across various programming languages. Although Codex itself is not open-source, we find that existing open-source models do achieve close results in some programming languages, although targeted mainly for natural language modeling. We further identify an important missing piece in the form of a large open-source model trained exclusively on a multi-lingual corpus of code. We release a new model, PolyCoder, with 2.7B parameters based on the GPT-2 architecture, which was trained on 249GB of code across 12 programming languages on a ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Programming Languages cs.PL URL: https://dx.doi.org/10.48550/arxiv.2202.13169 https://arxiv.org/abs/2202.13169 BASE Hide details 5 How Can We Know What Language Models Know? Jiang, Zhengbao; Xu, Frank F.; Araki, Jun... In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 423-438 (2020) (2020) BASE Show details 6 Data-to-Text Generation with Style Imitation ... Lin, Shuai; Wang, Wentao; Yang, Zichao. - : arXiv, 2019 BASE Show details 7 Empower Sequence Labeling with Task-Aware Neural Language Model ... Liu, Liyuan; Shang, Jingbo; Xu, Frank F.. - : arXiv, 2017 BASE Show details

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