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XGLUE is an evaluation benchmark XGLUE,which is composed of 11 tasks that span 19 languages. For each task, the training data is only available in English. This means that to succeed at XGLUE, a model must have a strong zero-shot cross-lingual transfer capability to learn from the English data of a specific task and transfer what it learned to other languages. Comparing to its concurrent work XTREME, XGLUE has two characteristics: First, it includes cross-lingual NLU and cross-lingual NLG tasks at the same time; Second, besides including 5 existing cross-lingual tasks (i.e. NER, POS, MLQA, PAWS-X and XNLI), XGLUE selects 6 new tasks from Bing scenarios as well, including News Classification (NC), Query-Ad Matching (QADSM), Web Page Ranking (WPR), QA Matching (QAM), Question Generation (QG) and News Title Generation (NTG). Such diversities of languages, tasks and task origin provide a comprehensive benchmark for quantifying the quality of a pre-trained model on cross-lingual natural language understanding and generation.

CodeXGLUE is a benchmark dataset and open challenge for code intelligence. It includes a collection of code intelligence tasks and a platform for model evaluation and comparison. CodeXGLUE stands for General Language Understanding Evaluation benchmark for CODE. It includes 14 datasets for 10 diversified code intelligence tasks covering the following scenarios: - code-code (clone detection, defect detection, cloze test, code completion, code repair, and code-to-code translation) - text-code (natural language code search, text-to-code generation) - code-text (code summarization) - text-text (documentation translation) A brief summary of CodeXGLUE is provided in the figure, including tasks, datasets, language, sizes in various states, baseline systems, providers, and short definitions of each task. Datasets highlighted in BLUE are newly introduced. Image source: https://github.com/microsoft/CodeXGLUE

Legal General Language Understanding Evaluation (LexGLUE) benchmark is a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. Image source: https://arxiv.org/pdf/2110.00976v1.pdf