JQL-AI/JQL-LLM-Edu-Annotations

📚 JQL Educational Quality Annotations from LLMs

This dataset provides 17,186,606 documents with high-quality LLM annotations for evaluating the educational value of web documents, and serves as a benchmark for training and evaluating multilingual LLM annotators as described in the JQL paper.

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📝 Dataset Summary

Multilingual document-level quality annotations scored on a 0–5 educational value scale by three state-of-the-art LLMs: Gemma-3-27B-it, Mistral-3.1-24B-it, and LLaMA-3.3-70B-it. Up to 500k documents per language from FineWeb2 are included. Annotations are aligned with human ratings and intended for quality estimation, distillation, and multilingual benchmark research.

🌐 Languages

In total we included 35 European languages. Input documents are in their native language, but models were prompted and responded in English.

🧱 Dataset Structure:

Name	Description
id	Unique FW2 identifier for the document
text	Full textual content extracted from the webpage
dum	Common Crawl dump identifier from which the data originates
url	Source URL of the document
date	Timestamp indicating when the document was crawled (ISO 8601 format)
file_path	Path to the WARC file in the Common Crawl S3 bucket
language	ISO 639-3 language code of the document (e.g., deu)
language_script	Script used in the document (e.g., Latn for Latin script)
language_score	Confidence score of the language identification (float between 0 and 1)
top_langs	JSON string mapping detected language-script pairs to their scores
minhash_cluster_size	Number of documents in the deduplication cluster
filter_reason	Reason for filtering or deduplication (e.g., duplicated_5_n_grams), NaN if it would have been filtered
edu_score	Dictionary with per-model aggregated scores (modelname_score), -1 if a invalid score was generated
aggregation	Dictionary with per-model aggregated type (modelname_type), either majority or average

✂️ Data Splits:

This dataset is not pre-split. Users can generate custom splits by:

• Language
• Model agreement
• Prediction validity
• Document length or other features

🎯 Intended Use

• Training multilingual document quality models
• Benchmarking multilingual LLM performance
• Distillation and teacher-student LLM training
• Creating filters for noisy web-scale data

⚠️ Limitations:

• LLM-generated scores, not human-authored
• Some predictions may be invalid or inconsistent
• No domain control across documents
• Educational value is a subjective, task-specific metric

📖 Citation

@article{ali2025judging,
    title     = {Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models},
    author    = {
      Mehdi Ali,
      Manuel Brack,
      Max Lübbering,
      Elias Wendt,
      Abbas Goher Khan,
      Richard Rutmann,
      Alex Jude,
      Maurice Kraus,
      Alexander Arno Weber,
      Felix Stollenwerk,
      David Kaczér,
      Florian Mai,
      Lucie Flek,
      Rafet Sifa,
      Nicolas Flores-Herr,
      Joachim Köhler,
      Patrick Schramowski,
      Michael Fromm,
      Kristian Kersting
    },
    year      = {2025},
    journal   = {arXiv preprint arXiv:2505:22232}
  }

🔗 Links:

• Base Dataset: FineWeb2
• Related Work: FineWeb2 LLM Judging Section