#evaluation #multi-metric #llm

GLUE (General Language Understanding Evaluation), is a comprehensive collection of resources used to evaluate the performance of language models across a broad spectrum of existing NLU tasks, ranging from sentiment analysis to textual entailment and more. GLUE does not impose constraints on the model architecture, allowing the exploration of various approaches and techniques.

The GLUE benchmark comprises datasets that vary in genre, size, and difficulty, ensuring a diverse range of text genres is covered. Some tasks have abundant training data, while others have limited data, encouraging models to represent linguistic knowledge in a way that supports efficient learning and effective knowledge transfer across tasks.

SuperGLUE was created to extend the boundaries of what NLU models can achieve by presenting them with more challenging tasks. It builds upon the foundation laid by GLUE, continuing the emphasis on diverse linguistic tasks and broad coverage of linguistic phenomena.

While GLUE focused largely on sentence-level tasks, SuperGLUE incorporates more tasks requiring reasoning about longer pieces of text, demanding a deeper understanding of language and logic from models.

Another important feature of SuperGLUE is the increased emphasis on tasks where models have to generate their own answers instead of selecting them from a given set, pushing models to demonstrate more advanced reasoning and language generation capabilities.

SuperGLUE also includes a benchmark for models to compete against human performance, with a dynamically updated leaderboard that tracks the progress of the field.

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