REPOGEO REPORT · LITE
lmmlzn/Awesome-LLMs-Datasets
Default branch main · commit ca0ab565 · scanned 6/20/2026, 5:32:47 PM
GitHub: 1,473 stars · 150 forks
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.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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 lmmlzn/Awesome-LLMs-Datasets, 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.
- highreadme#1Reposition README's opening to clarify it's a survey/list, not a dataset
Why:
CURRENT- Summarize existing representative LLMs text datasets across five dimensions: **Pre-training Corpora, Fine-tuning Instruction Datasets, Preference Datasets, Evaluation Datasets, and Traditional NLP Datasets**. (Regular updates)
COPY-PASTE FIXThis repository provides a comprehensive survey and curated list of existing representative LLMs text datasets, categorized across five dimensions: **Pre-training Corpora, Fine-tuning Instruction Datasets, Preference Datasets, Evaluation Datasets, and Traditional NLP Datasets**. (Regular updates)
- mediumhomepage#2Add a homepage URL linking to the associated survey paper
Why:
COPY-PASTE FIXhttps://arxiv.org/abs/2402.01769
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.
- Common Crawl · recommended 1×
- C4 (Colossal Clean Crawled Corpus) · recommended 1×
- OSCAR (Open Super-large Crawled Archive) · recommended 1×
- The Pile · recommended 1×
- Wikipedia · recommended 1×
- CATEGORY QUERYSeeking a curated list of datasets for pre-training and fine-tuning large language models.you: not recommendedAI recommended (in order):
- Common Crawl
- C4 (Colossal Clean Crawled Corpus)
- OSCAR (Open Super-large Crawled Archive)
- The Pile
- Wikipedia
- BookCorpus
- Alpaca (Stanford Alpaca dataset)
- ShareGPT
- Dolly 2.0 (Databricks Dolly 2.0 dataset)
- GLUE (General Language Understanding Evaluation)
- SuperGLUE
- SQuAD (Stanford Question Answering Dataset)
AI recommended 12 alternatives but never named lmmlzn/Awesome-LLMs-Datasets. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhich datasets are recommended for evaluating and enhancing retrieval-augmented generation LLMs?you: not recommendedAI recommended (in order):
- Natural Questions (NQ)
- TriviaQA
- MS MARCO
- FEVER
- ELI5
- HotpotQA
- BioASQ
AI recommended 7 alternatives but never named lmmlzn/Awesome-LLMs-Datasets. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 lmmlzn/Awesome-LLMs-Datasets?passAI did not name lmmlzn/Awesome-LLMs-Datasets — 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 lmmlzn/Awesome-LLMs-Datasets in production, what risks or prerequisites should they evaluate first?passAI named lmmlzn/Awesome-LLMs-Datasets 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 lmmlzn/Awesome-LLMs-Datasets solve, and who is the primary audience?passAI did not name lmmlzn/Awesome-LLMs-Datasets — 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?
Embed your GEO score
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lmmlzn/Awesome-LLMs-Datasets — 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