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      <h1 class="title is-1">🦊 JQL: Judging Quality across Languages</h1>
      <p class="subtitle is-5">Scalable and lightweight multilingual data filtering with LLM-based annotators</p>
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    <p>
      High-quality multilingual data is crucial for training effective large language models (LLMs).
      <strong>JQL (Judging Quality across Languages)</strong> is a scalable and lightweight multilingual data filtering approach that distills the judgment capabilities of strong 
      multilingual LLMs into efficient cross-lingual annotators. 
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      Overall, JQL improves data quality, retains more tokens, and generalizes to unseen languages. It outperforms heuristic baselines and enables cost-efficient multilingual pretraining data curation at scale.
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    <h2 class="title is-3">🧩 Main Pipeline Steps</h2>
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      <img src="https://cdn-uploads.huggingface.co/production/uploads/64bfc4d55ce3d382c05c0f9a/1zPQcwqt9Li_gCvd04_2_.png" alt="JQL Pipeline Overview">
      <figcaption><em>Figure 1: Overview of the JQL pipeline</em></figcaption>
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    <ol>
      <li><strong>📋 Ground Truth Creation:</strong> Human annotators label monolingual documents based on a structured instruction prompt. These documents are translated into all target languages to create a multilingual gold-standard dataset. (See Figure 1)</li>
      <li><strong>🤖 LLM-as-a-Judge Selection & Data Annotation:</strong> Strong multilingual LLMs (e.g., Gemma, Mistral, LLaMA) are evaluated against the ground truth, and top-performing models are used to produce synthetic annotations. (See Figure 1)</li>
      <li><strong>🪶 Lightweight Annotator Training:</strong> Train compact regression heads on frozen multilingual embeddings to create efficient, high-throughput annotators. (See Figure 1)</li>
      <li><strong>🚀 Scalable Data Filtering:</strong> Use trained annotators to filter large-scale pretraining corpora using quantile thresholds. (See Figure 1)</li>
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    <h2 class="title is-3">📊 Results</h2>
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      <li><strong>✔️ Accuracy:</strong> Spearman’s ρ > 0.87 with human ground truth</li>
      <li><strong>📈 Downstream LLM Training:</strong>
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          <li>+7.2% benchmark performance improvement</li>
          <li>+4.8% token retention vs. FineWeb2 heuristic filter</li>
          <li>Effective threshold strategies: 0.6 and 0.7 quantile</li>
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      <li><strong>⚡ Annotation Speed:</strong> ~11,000 docs/min (A100 GPU, avg. 690 tokens)</li>
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    <h2 class="title is-3">📁 Available Artifacts</h2>
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      <li>📄 Ground truth annotations in 35 languages</li>
      <li>🧠 Synthetic LLM-annotated dataset (14M+ documents)</li>
      <li>🪶 Lightweight annotation models:
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          <li>JQL-Gemma</li>
          <li>JQL-Mistral</li>
          <li>JQL-Llama</li>
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      <li>🛠️ Training & inference scripts (coming soon)</li>
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    <h2 class="title is-3">📜 Citation</h2>
    <p>If you use JQL, the annotations, or the pretrained annotators, please cite the paper:</p>
    <pre><code>@article{your2024jql,
  title={JQL: Judging Quality across Languages},
  author={Your, Name and Collaborators, Here},
  journal={Conference or preprint archive},
  year={2024}
}</code></pre>
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