🔬 BIG-bench: Quantifying Language Model Capabilities
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🔬 BIG-bench: Quantifying Language Model Capabilities
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This document introduces BIG-bench, a large and diverse benchmark designed to evaluate the capabilities of large language models across over two hundred challenging tasks. It highlights the limitations of existing benchmarks and argues for the necessity of more comprehensive assessments to understand the transformative potential of these models. The paper presents performance results for various models, including Google's BIG-G and OpenAI's GPT, alongside human rater baselines, revealing that while model performance generally improves with scale, it remains below human levels. Furthermore, the research explores aspects like model calibration, the impact of task phrasing, and the presence of social biases, offering insights into the strengths and weaknesses of current language models.