RRepoGEO

REPOGEO REPORT · LITE

stanford-crfm/helm

Default branch main · commit 63754d05 · scanned 6/23/2026, 8:41:59 AM

GitHub: 2,835 stars · 397 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)

2 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 stanford-crfm/helm, 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
    llm-evaluation, foundation-models, language-models, multimodal-models, benchmarks, python-framework, nlp, ai-evaluation, stanford-crfm, model-evaluation
  • highreadme#2
    Add a concise, high-level positioning statement to the README

    Why:

    COPY-PASTE FIX
    HELM is the definitive open-source platform for comprehensive, multi-dimensional evaluation of LLMs and foundation models.
  • mediumreadme#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

HELM distinguishes itself from other evaluation tools and frameworks by offering a truly holistic and multi-dimensional approach. While tools like Hugging Face Evaluate or EleutherAI/lm-evaluation-harness provide robust benchmarking capabilities, HELM integrates a broader spectrum of evaluation aspects, including diverse datasets, a unified model interface, a wide array of metrics beyond accuracy (e.g., efficiency, bias, toxicity), and interactive web UIs for detailed analysis and leaderboard comparisons. This comprehensive ecosystem ensures a more transparent and reproducible assessment of foundation 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
0 / 2
0% of queries surface stanford-crfm/helm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Evaluate / 🤗 Datasets
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Evaluate / 🤗 Datasets · recommended 1×
  2. ROUGE · recommended 1×
  3. BLEU · recommended 1×
  4. METEOR · recommended 1×
  5. BERTScore · recommended 1×
  • CATEGORY QUERY
    How can I systematically evaluate the performance of different large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Evaluate / 🤗 Datasets
    2. ROUGE
    3. BLEU
    4. METEOR
    5. BERTScore
    6. F1 Score
    7. Precision
    8. Recall
    9. Perplexity
    10. EleutherAI's LM Evaluation Harness
    11. Scale AI
    12. Appen
    13. Surveymonkey
    14. Google Forms
    15. HELM (Holistic Evaluation of Language Models)
    16. OpenAI Evals
    17. BigBench (Beyond the Imitation Game Benchmark)
    18. Anthropic's Constitutional AI
    19. Checklist

    AI recommended 19 alternatives but never named stanford-crfm/helm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python framework provides comprehensive benchmarks for foundation model evaluation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Evaluate (huggingface/evaluate)
    2. EleutherAI/lm-evaluation-harness (EleutherAI/lm-evaluation-harness)
    3. OpenAI Evals (openai/evals)
    4. DeepMind/tracr (deepmind/tracr)
    5. MLCommons/rdu_eval (mlcommons/rdu_eval)

    AI recommended 5 alternatives but never named stanford-crfm/helm. 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 stanford-crfm/helm?
    pass
    AI named stanford-crfm/helm explicitly

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

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

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

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stanford-crfm/helm — 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