Beyond the Imitation Game: How Big Language Models Really Behave
Researchers put together BIG-bench — a giant set of 204 tasks made by many people to see what language models can do now and might do soon.
The tests check simple facts, multi-step problems, common sense and social questions, and more.
As models grow in scale they get better at remembering facts, but people still beat them on many tasks, by quite a lot.
Some abilities climb slowly, others jump suddenly when the model reaches a certain size — those sudden jumps feel like a breakthrough but they can be fragile.
Different model designs act surprisingly similar, although some tricks help a bit.
One worry: bias often grows with size when the question is unclear, but small changes in the prompt can reduce that.
This work doesn't promise magic, it shows where progress is steady and where surprises may come.
It helps us prepare for new capabilities, and for making sure these systems behave safer and fairer, before they become widespread.
Read article comprehensive review in Paperium.net:
Beyond the Imitation Game: Quantifying and extrapolating the capabilities oflanguage models
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