The world's 7,000+ languages vary widely in the availability of resources for NLP, motivating efforts to systematically categorize them by their degree of resourcefulness (Joshi et al., 2020). A similar disparity exists among programming languages (PLs); however, no resource-tier taxonomy has been established for code. As large language models (LLMs) grow increasingly capable of generating code, such a taxonomy becomes essential. To fill this gap, we present the first reproducible PL resource classification, grouping 646 languages into four tiers. We show that only 1.9% of languages (Tier 3, High) account for 74.6% of all tokens in seven major corpora, while 71.7% of languages (Tier 0, Scarce) contribute just 1.0%. Statistical analyses of within-tier inequality, dispersion, and distributional skew confirm that this imbalance is both extreme and systematic. Our results provide a principled framework for dataset curation and tier-aware evaluation of multilingual LLMs.
Towards No-Code Programming of Cobots: Experiments with Code Synthesis by Large Code Models for Conversational Programming
Computational Linguistics, Department of Linguistics, University of Potsdam, Germany（波恩大学语言学系计算语言学部门）；German Research Center for Artificial Intelligence (DFKI), Berlin, Germany（德国人工智能研究中心（DFKI））
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Jisuan Institute of Technology, Beijing JudaoYouda Network Technology Co. Ltd.（智算技术研究所，北京judaoYouda网络技术有限公司）
