BIG-bench

BIG-bench, short for Beyond the Imitation Game Benchmark, is a collaborative LLM evaluation benchmark released in 2022 with contributions from 444 authors at 132 institutions. The benchmark contains over 200 tasks covering a remarkable diversity of skills: logical reasoning, social bias, mathematical induction, theory of mind, multilingual capabilities, programming, and many domains far outside standard NLP. Tasks range from straightforward (factual recall) to highly creative (predicting movie plots, generating recipes), with many designed specifically to probe capabilities thought to be near or beyond the frontier. BIG-bench's diversity made it widely cited in scaling-law papers studying how capabilities emerge with model size and training compute. The lighter-weight BIG-bench Hard (BBH) subset focuses on 23 challenging tasks where models historically struggled, and has become a more practical benchmark for newer models that have saturated easier BIG-bench tasks. AI governance teams use BIG-bench tasks for targeted capability testing rather than as a single overall metric. The benchmark is hosted on GitHub at github.com/google/BIG-bench under permissive licensing.

BIG-bench-validated models with Centralpoint: Centralpoint routes to models validated across BIG-bench, MMLU, HELM, and other benchmarks in a model-agnostic stack with consistent metering. The platform keeps prompts local, supports generative and embedded models, and deploys chatbots through one line of JavaScript on any portal.


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