Beyond the Imitation Game: Quantifying and extrapolating the capabilities oflanguage models

Source: Dev.to

Summary

• Researchers assembled BIG-bench, a collection of 204 tasks created by many contributors to evaluate current and future language model capabilities.
• The tasks cover factual recall, multi‑step reasoning, common sense, social questions, and more.
• As model scale increases, performance on factual recall improves, yet humans still outperform models on many tasks by a large margin.
• Some abilities improve gradually, while others exhibit sudden jumps at certain model sizes—these “breakthroughs” can be fragile.
• Different model architectures behave surprisingly similarly, though certain techniques provide modest gains.
• A concern: bias often amplifies with scale on ambiguous queries, though small prompt adjustments can mitigate it.
• The work does not claim magical solutions; it highlights areas of steady progress and potential surprises.
• It aims to help prepare for emerging capabilities and ensure safer, fairer behavior before widespread deployment.

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models