RRepoGEO

REPOGEO REPORT · LITE

mlfoundations/dclm

Default branch main · commit 361714bd · scanned 6/18/2026, 11:22:48 PM

GitHub: 1,447 stars · 133 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 mlfoundations/dclm, 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
    Add a clear, concise introductory paragraph to the README

    Why:

    CURRENT
    # DataComp-LM (DCLM)

## ⚠️ Updates to centered CORE and EXTENDED calculations (9/5/2025)
    COPY-PASTE FIX
    # DataComp-LM (DCLM)

DataComp-LM (DCLM) is a comprehensive benchmark suite designed to evaluate and compare large language models (LLMs) across various datasets and tasks. It provides tools and metrics to understand the impact of different training data compositions on LLM performance, enabling researchers to reliably benchmark and analyze model capabilities.

## ⚠️ Updates to centered CORE and EXTENDED calculations (9/5/2025)
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    large-language-models, llm-benchmarking, language-model-evaluation, datacomp, machine-learning, nlp, evaluation-framework
  • mediumfaq#3
    Add a basic FAQ section to the README

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ## Frequently Asked Questions

### What is DataComp-LM (DCLM) for?
DataComp-LM (DCLM) is designed for comprehensive benchmarking and evaluation of large language models (LLMs), with a focus on analyzing the impact of training data composition.

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 mlfoundations/dclm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MLflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MLflow · recommended 2×
  2. LM Evaluation Harness (lm-eval) · recommended 1×
  3. OpenAI Evals · recommended 1×
  4. Hugging Face Evaluate · recommended 1×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    How to reliably benchmark large language models across various datasets and tasks?
    you: not recommended
    AI recommended (in order):
    1. LM Evaluation Harness (lm-eval)
    2. OpenAI Evals
    3. Hugging Face Evaluate
    4. LangChain
    5. DeepEval
    6. MLflow

    AI recommended 6 alternatives but never named mlfoundations/dclm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for comparing language model performance given different training data compositions?
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Weights & Biases (W&B)
    3. Comet ML
    4. TensorBoard
    5. Neptune.ai
    6. Pandas
    7. Matplotlib
    8. Seaborn

    AI recommended 8 alternatives but never named mlfoundations/dclm. 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 mlfoundations/dclm?
    pass
    AI named mlfoundations/dclm explicitly

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

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

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

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mlfoundations/dclm — 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