---
title: 'Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2206-04615-big-bench.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Aarohi Srivastava and 450 coauthors (listed on the arXiv abs page), Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: evals'
tags: arxiv, evals, benchmarks, language-models
arxiv_id: 2206.04615
alias: 2206.04615-big-bench
type: pointer
format: pointer
sources:
  - id: S1
    title: 'Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models'
    publisher: arXiv
    authors: 'Aarohi Srivastava and 450 coauthors (listed on the arXiv abs page)'
    url: https://arxiv.org/abs/2206.04615
    version_read: 'v3 (12 Jun 2023)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

## Summary
[Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) (2022) is the BIG-bench paper. It gathered a large, diverse set of tasks, many of them unusual compared with standard NLP datasets, to measure what language models can do as they scale, including the later BIG-Bench Hard slice that is still widely reported.

What it introduced: a collaborative benchmark of hundreds of tasks, with a framework for adding new ones, and an analysis of how performance changes with model scale on that mix.

What it led to: a common external eval suite, and the BIG-Bench Hard subset used in many later model reports (including reasoning-model papers). Live notes on evaluation practice include [ao-evals-agent-benchmarks-and-judges](https://iinventedlight.com/library/notes/ao-evals-agent-benchmarks-and-judges/).

Where to read it: https://arxiv.org/abs/2206.04615 (v3, last revised 12 Jun 2023).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).