✨Qwen3 Technical Report

📝 Summary:
Qwen3 is a new series of large language models integrating thinking and non-thinking modes for unified performance and efficiency. It achieves state-of-the-art results across diverse tasks and expands multilingual support to 119 languages.

🔹 Publication Date: Published on May 14

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/qwen3-technical-report
• PDF: https://arxiv.org/pdf/2505.09388
• Project Page: https://qwenlm.github.io/blog/qwen3/
• Github: https://github.com/QwenLM/Qwen3

🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct
• https://huggingface.co/Qwen/Qwen3-235B-A22B
• https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct

✨ Spaces citing this paper:
• https://huggingface.co/spaces/modelscope/DocResearch
• https://huggingface.co/spaces/enzostvs/deepsite
• https://huggingface.co/spaces/multimodalart/Eigen-Banana

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✓ https://t.me/DataScienceT

#LLM #AI #MultilingualAI #NLP #Qwen3

✨Jina-VLM: Small Multilingual Vision Language Model

📝 Summary:
Jina-VLM is a 2.4B vision-language model achieving top multilingual VQA among open 2B-scale models. It couples a SigLIP2 vision encoder with a Qwen3 language backbone via an attention-pooling connector for efficient arbitrary-resolution image processing.

🔹 Publication Date: Published on Dec 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04032
• PDF: https://arxiv.org/pdf/2512.04032

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#VLM #MultilingualAI #ComputerVision #DeepLearning #VQA

✨Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates

📝 Summary:
Source-Shielded Updates (SSU) enables the adaptation of instruct LLMs to new languages using only unlabeled data, preserving source knowledge and achieving competitive target-language performance. AI-...

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04844
• PDF: https://arxiv.org/pdf/2512.04844
• Github: https://github.com/gucci-j/ssu

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#LLM #NLP #MachineLearning #CatastrophicForgetting #MultilingualAI

✨CLINIC: Evaluating Multilingual Trustworthiness in Language Models for Healthcare

📝 Summary:
CLINIC is a multilingual benchmark evaluating language model trustworthiness in healthcare across 15 languages and five dimensions. It finds that LMs struggle with factual correctness, demonstrate bias, and are vulnerable to privacy breaches and attacks. This work highlights shortcomings to impro...

🔹 Publication Date: Published on Dec 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11437
• PDF: https://arxiv.org/pdf/2512.11437

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#AI #HealthcareAI #LLM #AISafety #MultilingualAI

✨FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition

📝 Summary:
FiNERweb is a new pipeline that scales multilingual Named Entity Recognition dataset creation to 91 languages using LLMs. It produces 225k high-quality passages, enabling models to achieve comparable or improved zero-shot performance with 19x less data.

🔹 Publication Date: Published on Dec 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13884
• PDF: https://arxiv.org/pdf/2512.13884
• Github: https://github.com/whoisjones/FiNERweb

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#NER #NLP #LLMs #MultilingualAI #Datasets