REPOGEO REPORT · LITE

google/BIG-bench

Default branch main · commit 092b196c · scanned 6/27/2026, 12:32:56 PM

GitHub: 3,248 stars · 615 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface google/BIG-bench, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

large-language-models, llm-evaluation, benchmark, ai, machine-learning, nlp, language-models, evaluation-framework

highreadme#2

Strengthen the README's opening sentence to emphasize its role as an LLM evaluation platform

Why:

CURRENT

The Beyond the Imitation Game Benchmark (BIG-bench) is a *collaborative* benchmark intended to probe large language models and extrapolate their future capabilities.

COPY-PASTE FIX

BIG-bench (Beyond the Imitation Game Benchmark) is a comprehensive, collaborative evaluation platform designed to rigorously benchmark and extrapolate the capabilities of large language models (LLMs).

mediumreadme#3

Add a "Comparison with Alternatives" section to the README

Why:

COPY-PASTE FIX

## Comparison with Alternatives

BIG-bench stands apart from other LLM evaluation frameworks like EleutherAI's LM Evaluation Harness or OpenAI Evals by offering a uniquely broad and diverse collection of over 200 tasks. Our collaborative, community-driven approach focuses on probing emergent abilities and guiding future research, rather than solely providing standard performance metrics. This extensive task diversity allows for a deeper exploration of model capabilities beyond typical benchmarks.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface google/BIG-bench

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI Evals

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenAI Evals · recommended 2×
EleutherAI/lm-evaluation-harness · recommended 1×
Hugging Face Evaluate library · recommended 1×
HELM · recommended 1×
LangChain · recommended 1×

CATEGORY QUERY
How can I effectively benchmark and evaluate the capabilities of large language models?
you: not recommended
AI recommended (in order):
1. EleutherAI/lm-evaluation-harness (EleutherAI/lm-evaluation-harness)
2. OpenAI Evals
3. Hugging Face Evaluate library
4. HELM
5. LangChain
6. DeepEval
7. Scale AI
8. Appen
AI recommended 8 alternatives but never named google/BIG-bench. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What platforms offer diverse tasks for probing and comparing large language model performance?
you: not recommended
AI recommended (in order):
1. Hugging Face Datasets and Evaluate
2. EleutherAI's LM Evaluation Harness
3. OpenAI Evals
4. BigBench
5. MMLU
6. GLUE and SuperGLUE
AI recommended 6 alternatives but never named google/BIG-bench. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of google/BIG-bench?
pass
AI named google/BIG-bench explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts google/BIG-bench in production, what risks or prerequisites should they evaluate first?
pass
AI named google/BIG-bench explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo google/BIG-bench solve, and who is the primary audience?
pass
AI named google/BIG-bench explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of google/BIG-bench. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/google/BIG-bench.svg)](https://repogeo.com/en/r/google/BIG-bench)

HTML

<a href="https://repogeo.com/en/r/google/BIG-bench"><img src="https://repogeo.com/badge/google/BIG-bench.svg" alt="RepoGEO" /></a>

Pro

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google/BIG-bench — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite