Update README.md

README.md

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-# 🦊 JQL: Judging Quality across Languages
+**Jackal-AI** is a community of researchers committed to advancing the development of **multilingual foundation models**. We focus on open methods, reproducible experiments, and practical tools to improve multilingual training, alignment, and reasoning at scale.
 
-High-quality multilingual data is crucial for training effective large language models (LLMs).  
-**JQL (Judging Quality across Languages)** is a scalable and lightweight data filtering approach
-that distills the judgment capabilities of strong multilingual LLMs into efficient cross-lingual annotators. 
-These annotators enable robust filtering of web-scale data.
+## Latest Research
 
-JQL improves data quality, retains more tokens, and generalizes beyond high-resource European languages—achieving strong performance
-on Arabic, Thai, and Mandarin. 
-It outperforms heuristic baselines and enables efficient multilingual pretraining data curation at scale.
+- [Judging Across Languages]()
+- [Tokenizer Choice For LLM Training: Negligible or Crucial?](https://aclanthology.org/2024.findings-naacl.247/)
+- [Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions?](https://aclanthology.org/2024.emnlp-main.1159/)
+- [Do Multilingual Large Language Models Mitigate Stereotype Bias?](https://aclanthology.org/2024.c3nlp-1.6.pdf)
 
----
-
-## 🧩 Main Pipeline Steps
-
-
-![JQL Pipeline Overview](https://cdn-uploads.huggingface.co/production/uploads/64bfc4d55ce3d382c05c0f9a/AZ7NrNQ2RuRFcIQ63L5Jv.png)
-
-1. **📋 Ground Truth Creation**  
-   Human annotators label monolingual documents based on a structured instruction prompt.
-   These documents are then translated into all target languages to form a multilingual gold-standard dataset.  
-   *(See Figure 1)*
-
-3. **🤖 LLM-as-a-Judge Selection & Data Annotation**  
-   Several strong multilingual LLMs (e.g., Gemma-3-27B-it, Mistral-3.1-24B-it, LLaMA-3.3-70B-it) are evaluated against the GT dataset.
-   The top-$n$ models are selected to generate high-quality synthetic annotations on a large multilingual corpus.  
-   *(See Figure 1)*
-
-5. **🪶 Lightweight Annotator Training**  
-   Regression heads are trained on top of frozen multilingual embeddings (e.g., Snowflake Arctic Embed v2) using the synthetic data.
-   This results in lightweight, efficient annotators capable of high-throughput filtering.  
-   *(See Figure 1)*
-
-7. **🚀 Scalable Data Filtering**  
-   The trained annotators are used to label large-scale pretraining corpora.
-   Quality-based thresholds (e.g., 0.6 or 0.7 quantile) are applied to retain high-quality subsets for downstream LLM training.  
-   *(See Figure 1)*
-
----
-
-## 📊 Results
-
-- **Accuracy**: Spearman’s ρ > 0.87 with human ground truth  
-- **Downstream LLM Training**:  
-  - Up to **+7.2% benchmark performance improvement**
-  - **+4.8% token retention** over baseline FineWeb2 heuristic filter  
-  - Reliable trade-offs with 0.6 and 0.7 quantile filtering strategies
-- **Annotation Speed**: ~11,000 docs/min (A100 GPU, 690 tokens avg. length)  
-
----
-
-
-## 📁 Available Artifacts
-
-- ✅ Ground truth annotations in 35 languages  
-- ✅ Synthetic LLM-annotated dataset (14M+ documents)  
-- ✅ Lightweight annotation models:  
-  - `JQL-Gemma`  
-  - `JQL-Mistral`  
-  - `JQL-Llama`  
-- ✅ Training & inference scripts *(coming soon)*
-
----
-
-## 📜 Citation
-
-If you use JQL, the annotations, or the pretrained annotators, please cite the paper:
+---