BIG-bench

BIG-bench
Name	BIG-bench
Type	Benchmark
Field	Artificial intelligence
Launched	2021
Developers	Google Research, Anthropic, OpenAI
Languages	Multilingual
License	Open-source

BIG-bench

BIG-bench is a large-scale benchmarking suite developed to probe capabilities and limitations of advanced large language models. It aggregates diverse tasks contributed by researchers across academia and industry to test reasoning, knowledge, creativity, and alignment properties in contemporary models. The suite served as a focal point for comparative evaluation among research groups working on general-purpose transformer models and generative pretraining.

Overview

BIG-bench originated as a collaborative effort involving teams from Google Research, Anthropic, and OpenAI alongside contributors from institutions such as DeepMind, MIT, Stanford University, Carnegie Mellon University, and University of California, Berkeley. It was designed to complement benchmarks like GLUE, SuperGLUE, SQuAD, ImageNet, and COCO by focusing on tasks that stress emergent capabilities observed in models such as GPT-3, PaLM, BERT, T5, and Chinchilla. The project drew contributions from researchers affiliated with Columbia University, Harvard University, University of Oxford, University of Cambridge, and industrial labs including Microsoft Research, Facebook AI Research, and NVIDIA.

Design and Tasks

The suite comprises hundreds of tasks spanning reasoning, analogy, commonsense, math, logic, code, translation, and social judgment. Contributors included teams from University of Toronto, ETH Zurich, University of Washington, Georgia Institute of Technology, and New York University. Task formats mirror challenges found in datasets like Winograd Schema Challenge, HellaSwag, DROP, ARC (AI2 Reasoning Challenge), and BIG-bench HARD. Examples include puzzle-style prompts, adversarially crafted items, and open-ended generation requiring factual grounding similar to problems tackled by models in NeurIPS, ICLR, and ACL conferences. Several tasks were designed to probe failure modes related to phenomena studied at AAAI workshops and in papers from Nature, Science, and Proceedings of the Royal Society.

Evaluation Methodology

Evaluations used both automated metrics and human judgments from panels with affiliations to University of Edinburgh, University of Michigan, Princeton University, and Cornell University. Automated scoring employed metrics akin to BLEU, ROUGE, METEOR, and embedding-based similarity measures used in research at Google Brain and OpenAI. Calibration and significance testing drew on techniques presented at ICML and statistical methods from scholars at Bell Labs and IBM Research. Leaderboards and result aggregation were maintained in collaboration with platforms used by Kaggle, Papers with Code, and repositories hosted on GitHub.

Results and Findings

Analyses revealed that very large models such as GPT-3, PaLM, and later generations exhibited emergent behavior on many tasks, outperforming smaller baselines from efforts at Allen Institute for AI and Salesforce Research on certain challenges but failing on others that required robust causal reasoning or tool use. Studies documented performance disparities similar to those explored in research by Yoshua Bengio, Geoffrey Hinton, and Yann LeCun regarding scaling laws. Results informed debates at venues including NeurIPS, ICLR, and panels at AAAI about capability generalization, robustness, and the need for specialized evaluation found in work by Timnit Gebru and Margaret Mitchell on model behavior and bias.

Limitations and Criticisms

Critiques pointed to issues raised by scholars from University of California, Los Angeles, Johns Hopkins University, University of Pennsylvania, and ethics groups associated with Center for Humane Technology and AI Now Institute: task representativeness, difficulty in aligning automated metrics with human values, and potential for overfitting to benchmark-specific heuristics. Concerns echoed earlier critiques of benchmarks like ImageNet and GLUE about dataset bias, labeling noise, and ecological validity discussed at FAccT and in commentary by researchers at Mozilla Foundation and Electronic Frontier Foundation.

Impact and Adoption

BIG-bench influenced model evaluation practices at industrial labs including Google DeepMind, OpenAI, Anthropic, Meta Platforms, Inc., and academic groups at MIT-IBM Watson AI Lab. It informed regulatory discussions at bodies such as European Commission, UK Department for Science, Innovation and Technology, and policy forums involving United Nations panels on AI governance. The benchmark spurred follow-up efforts integrating human-in-the-loop evaluation and adversarial task design, cited in publications from University of Toronto Scarborough, McGill University, and Peking University.

Related Benchmarks and Successors

Subsequent benchmarks and suites built on its approach, including proposals and datasets developed by teams from Hugging Face, EleutherAI, OpenReview, BigScience, and initiatives such as MMLU, HellaSwag, HumanEval, and the Massive Multitask Language Understanding evaluations. Research continuations were presented at conferences including NeurIPS, ICML, ACL, and workshops organized by OpenAI Scholars and community groups like Papers with Code.

Category:Artificial intelligence benchmarks