RRepoGEO

REPOGEO REPORT · LITE

KLUE-benchmark/KLUE

Default branch main · commit 3efd9870 · scanned 6/7/2026, 7:26:49 AM

GitHub: 595 stars · 59 forks

AI VISIBILITY SCORE
74 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
Rule findings
2 pass · 0 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 KLUE-benchmark/KLUE, 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
  • highreadme#1
    Emphasize 'comprehensive' and 'multi-task' nature in README opening

    Why:

    CURRENT
    The KLUE is introduced to make advances in Korean NLP. Korean pre-trained language models (PLMs) have appeared to solve Korean NLP problems since PLMs have brought significant performance gains in NLP problems in other languages. Despite the proliferation of Korean language models, however, none of the proper evaluation datasets has been opened yet. The lack of such benchmark dataset limits the fair comparison between the models and further progress on model architectures.
    COPY-PASTE FIX
    KLUE is a comprehensive, multi-task benchmark introduced to advance Korean NLP by providing standardized evaluation for pre-trained language models (PLMs). Despite the proliferation of Korean language models, a proper, comprehensive evaluation dataset has been lacking, limiting fair comparison and further progress. KLUE addresses this by offering diverse tasks and data.
  • mediumtopics#2
    Add a topic to highlight multi-task nature

    Why:

    CURRENT
    benchmark, bert, korean, korean-nlp, roberta
    COPY-PASTE FIX
    benchmark, bert, korean, korean-nlp, roberta, multi-task-benchmark
  • lowreadme#3
    Add a brief 'Comparison with other Korean NLP benchmarks' section

    Why:

    COPY-PASTE FIX
    ## Comparison with other Korean NLP benchmarks
While several valuable Korean NLP datasets exist (e.g., KorNLI/KorSTS for specific tasks, KorQuAD for QA), KLUE stands out as a comprehensive, multi-task benchmark. Unlike single-task datasets, KLUE integrates 8 diverse tasks, providing a holistic evaluation framework for Korean Language Understanding models.

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
1 / 2
50% of queries surface KLUE-benchmark/KLUE
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
KorNLI/KorSTS
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. KorNLI/KorSTS · recommended 2×
  2. Naver NLP Challenge Datasets · recommended 1×
  3. KorQuAD · recommended 1×
  4. NSMC · recommended 1×
  5. HateSpeech-KR · recommended 1×
  • CATEGORY QUERY
    How to benchmark different Korean natural language understanding models effectively?
    you: #1
    AI recommended (in order):
    1. KLUE ← you
    2. KorNLI/KorSTS
    3. Naver NLP Challenge Datasets
    4. KorQuAD
    5. NSMC
    6. HateSpeech-KR
    7. AI Hub Datasets
    Show full AI answer
  • CATEGORY QUERY
    What are the best comprehensive evaluation benchmarks for Korean language models?
    you: not recommended
    AI recommended (in order):
    1. KLUE (Korean Language Understanding Evaluation)
    2. KorNLI/KorSTS
    3. Naver NLP Challenge
    4. Kakao Brain's KoBART/KoGPT benchmarks
    5. AI Hub Korean Datasets

    AI recommended 5 alternatives but never named KLUE-benchmark/KLUE. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 KLUE-benchmark/KLUE?
    pass
    AI named KLUE-benchmark/KLUE explicitly

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

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

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

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