RRepoGEO

REPOGEO REPORT · LITE

google-research/xtreme

Default branch master · commit 838c13b6 · scanned 5/31/2026, 11:57:20 PM

GitHub: 652 stars · 110 forks

AI VISIBILITY SCORE
81 /100
Healthy
Category recall
2 / 2
Avg rank #1.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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-research/xtreme, 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

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

OVERALL DIRECTION
  • mediumreadme#1
    Explicitly state XTREME's unique differentiator in the README

    Why:

    COPY-PASTE FIX
    Add a sentence to the 'Introduction' or a new 'Why XTREME?' section that explicitly positions XTREME's unique value against other benchmarks. For example: 'Unlike other benchmarks such as XTREME-R, TyDi QA, MLQA, or XNLI, XTREME offers a uniquely comprehensive evaluation across 40 typologically diverse languages and nine distinct NLP tasks, providing a broader and deeper assessment of cross-lingual generalization.'
  • lowreadme#2
    Add direct link to the XTREME website in the README header

    Why:

    CURRENT
    **Website**
    COPY-PASTE FIX
    [**Website**](https://sites.research.google/xtreme)

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
2 / 2
100% of queries surface google-research/xtreme
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
14%
Of all named tools, what % are you?
Top rival
XTREME-R
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. XTREME-R · recommended 2×
  2. TyDi QA · recommended 2×
  3. MLQA · recommended 1×
  4. WikiANN · recommended 1×
  5. Cross-lingual Natural Language Inference (XNLI) · recommended 1×
  • CATEGORY QUERY
    How can I benchmark the cross-lingual generalization of my multilingual NLP models?
    you: #1
    AI recommended (in order):
    1. Xtreme ← you
    2. XTREME-R
    3. TyDi QA
    4. MLQA
    5. WikiANN
    6. Cross-lingual Natural Language Inference (XNLI)
    7. Universal Dependencies
    Show full AI answer
  • CATEGORY QUERY
    Looking for a comprehensive benchmark to evaluate multilingual models across diverse languages and tasks.
    you: #1
    AI recommended (in order):
    1. XTREME ← you
    2. XTREME-R
    3. M3Exam
    4. TyDi QA
    5. mGLUE
    6. XNLI
    7. MASSIVE
    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-research/xtreme?
    pass
    AI did not name google-research/xtreme — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts google-research/xtreme in production, what risks or prerequisites should they evaluate first?
    pass
    AI named google-research/xtreme 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-research/xtreme solve, and who is the primary audience?
    pass
    AI named google-research/xtreme explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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google-research/xtreme — 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