BIG-bench: A 204-Task Benchmark for LLM Evaluation

BIG-bench: A 204-Task Benchmark for LLM Evaluation

TL;DR

BIG-bench is a collaborative benchmark designed to evaluate language models across 204 diverse tasks. Created by 450 authors across 132 institutions, it covers reasoning, mathematics, knowledge, language understanding, creativity, programming, and social bias.

Now available on AIOZ AI, BIG-bench helps researchers identify model strengths and failure modes beyond a single aggregate score.

What BIG-bench Is

BIG-bench stands for the Beyond the Imitation Game Benchmark.

It was created to probe the current capabilities of language models and explore how those capabilities change as models scale.

Its tasks span areas including:

  • Mathematical and logical reasoning
  • Common-sense understanding
  • Linguistics and language knowledge
  • Biology and physics
  • Creativity and symbolic manipulation
  • Social reasoning and bias

How the Benchmark Is Organized

BIG-bench contains 204 tasks, with 167 JSON-based tasks included in the AIOZ AI dataset distribution.

Each JSON record can contain:

  • idx: example index within the task
  • inputs: prompt or question presented to the model
  • targets: accepted answer or answers
  • multiple_choice_targets: available answer choices
  • multiple_choice_scores: labels identifying the correct choices

Tasks can use free-form generation, multiple-choice evaluation, or programmatic scoring. Because each task was created and reviewed independently, formats, metrics, and documentation may vary across the collection.

Each task provides default, train, and validation splits. The default split contains all available samples and is recommended for evaluation. The training split is intended for few-shot examples rather than model fine-tuning.

Beyond a Single Benchmark Score

A single evaluation score can hide major differences in model behavior. BIG-bench supports task-level capability profiling, allowing researchers to:

  • Compare performance across distinct skills
  • Identify task-specific failure patterns
  • Measure how capabilities change with model scale
  • Evaluate prompting and instruction-tuning strategies
  • Examine bias under ambiguous conditions
  • Compare different model architectures and training approaches

The task files also contain a canary string intended to reduce their accidental inclusion in web-scraped training datasets, supporting more contamination-aware evaluation.

BIG-bench has also supported the development of smaller and more difficult evaluation suites.

  • BIG-bench Lite: A curated subset of 24 diverse JSON tasks for more efficient evaluation
  • BIG-bench Hard: A collection of 23 challenging tasks on which earlier language models did not outperform average human raters

BIG-bench Hard is commonly used to evaluate multi-step reasoning and the effect of chain-of-thought prompting.

Key Dataset Details

  • Dataset: BIG-bench
  • Full name: Beyond the Imitation Game Benchmark
  • Dataset type: language model evaluation benchmark
  • Total tasks: 204
  • JSON tasks on AIOZ AI: 167
  • BIG-bench Lite: 24 tasks
  • BIG-bench Hard: 23 tasks
  • Contributors: 450 authors
  • Institutions: 132
  • License: Apache License 2.0
  • Intended purpose: evaluation, not model training

What Builders Should Know

BIG-bench is predominantly English, although individual tasks extend into many other written, synthetic, and programming languages. This multilingual coverage is uneven and should not be treated as a balanced language benchmark.

Using benchmark examples for model training can contaminate future evaluations. The train split should therefore be reserved for few-shot prompting and task demonstrations.

Access It on AIOZ AI

204 tasks, 450 authors, and 132 institutions: BIG-bench provides a broad evaluation surface for understanding what a language model can and cannot do.

Access BIG-bench on AIOZ AI and start evaluating model capabilities task by task.

FAQ

Q1: What is BIG-bench used for?

It is used to evaluate language models across diverse capabilities.

Q2: How many tasks does BIG-bench contain?

The complete benchmark contains 204 tasks. The AIOZ AI distribution includes 167 JSON-based tasks.

Q3: Can BIG-bench be used for model training?

It is designed for evaluation. The train split provides examples for few-shot prompting and should not be used to fine-tune models on benchmark answers.