GLUE is a benchmark and analysis platform for natural language understanding (NLU), consisting of nine tasks covering diverse domains, data sizes, and difficulties. The tasks include question answering, sentiment analysis, textual entailment, and others, with a focus on evaluating models' ability to generalize across tasks. GLUE includes a diagnostic dataset for detailed linguistic analysis and an online platform for model evaluation and comparison. The benchmark uses pre-existing datasets, some with private test data to ensure fairness. It also includes hand-crafted examples to probe models' understanding of linguistic phenomena. Baseline models based on transfer and representation learning are evaluated, showing that multi-task training outperforms single-task models. However, the best model still achieves low performance, indicating the need for improved NLU systems. GLUE also includes a diagnostic dataset to analyze models' performance on specific linguistic challenges. The benchmark results show that multi-task models perform better than single-task models, but current models still struggle with many linguistic tasks. Analysis of the diagnostic dataset reveals that models often fail to handle complex linguistic phenomena, suggesting areas for future research. GLUE provides a platform for evaluating and analyzing NLU systems, highlighting the need for more generalizable models.GLUE is a benchmark and analysis platform for natural language understanding (NLU), consisting of nine tasks covering diverse domains, data sizes, and difficulties. The tasks include question answering, sentiment analysis, textual entailment, and others, with a focus on evaluating models' ability to generalize across tasks. GLUE includes a diagnostic dataset for detailed linguistic analysis and an online platform for model evaluation and comparison. The benchmark uses pre-existing datasets, some with private test data to ensure fairness. It also includes hand-crafted examples to probe models' understanding of linguistic phenomena. Baseline models based on transfer and representation learning are evaluated, showing that multi-task training outperforms single-task models. However, the best model still achieves low performance, indicating the need for improved NLU systems. GLUE also includes a diagnostic dataset to analyze models' performance on specific linguistic challenges. The benchmark results show that multi-task models perform better than single-task models, but current models still struggle with many linguistic tasks. Analysis of the diagnostic dataset reveals that models often fail to handle complex linguistic phenomena, suggesting areas for future research. GLUE provides a platform for evaluating and analyzing NLU systems, highlighting the need for more generalizable models.