✨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

✨Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection

📝 Summary:
A new benchmark, mfmdscen, evaluates behavioral biases in large language models for multilingual financial misinformation detection. It uses complex economic scenarios and a multilingual dataset, revealing significant biases across 22 mainstream LLMs.

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05403
• PDF: https://arxiv.org/pdf/2601.05403

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#LLM #AIbias #FinancialAI #MisinformationDetection #MultilingualAI

✨dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model

📝 Summary:
dots.ocr is a unified Vision-Language Model that jointly learns document layout parsing tasks, overcoming limitations of multi-stage pipelines. It achieves state-of-the-art performance on OmniDocBench and sets a new baseline on the challenging multilingual XDocParse benchmark.

🔹 Publication Date: Published on Dec 2, 2025

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/dotsocr-multilingual-document-layout-parsing-in-a-single-vision-language-model
• PDF: https://arxiv.org/pdf/2512.02498
• Github: https://github.com/rednote-hilab/dots.ocr

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#VisionLanguageModel #DocumentParsing #MultilingualAI #AIResearch #DeepLearning

✨Language of Thought Shapes Output Diversity in Large Language Models

📝 Summary:
Controlling the language of thought in large language models increases output diversity. Switching the internal thinking language from English to non-English languages consistently boosts diversity, with mixed-language sampling yielding superior results. This approach expands LLMs diversity ceili...

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11227
• PDF: https://arxiv.org/pdf/2601.11227
• Github: https://github.com/iNLP-Lab/Multilingual-LoT-Diversity

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#LLM #AI #NLP #MultilingualAI #OutputDiversity

✨LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR

📝 Summary:
LightOnOCR-2-1B is a 1B-parameter end-to-end multilingual vision-language model for OCR. It converts document images to text, achieving state-of-the-art results while being smaller and faster. It also features improved image localization and robustness.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14251
• PDF: https://arxiv.org/pdf/2601.14251

🔹 Models citing this paper:
• https://huggingface.co/lightonai/LightOnOCR-1B-1025
• https://huggingface.co/lightonai/LightOnOCR-2-1B
• https://huggingface.co/lightonai/LightOnOCR-0.9B-32k-1025

✨ Datasets citing this paper:
• https://huggingface.co/datasets/lightonai/LightOnOCR-mix-0126
• https://huggingface.co/datasets/lightonai/LightOnOCR-bbox-mix-0126

✨ Spaces citing this paper:
• https://huggingface.co/spaces/lightonai/LightOnOCR-2-1B-Demo
• https://huggingface.co/spaces/lightonai/LightOnOCR-1B-Demo
• https://huggingface.co/spaces/lightonai/LightOnOCR-1B-Demo-zero

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#OCR #VisionLanguageModel #AI #DeepLearning #MultilingualAI

✨PingPong: A Natural Benchmark for Multi-Turn Code-Switching Dialogues

📝 Summary:
PingPong is a new human-authored benchmark for natural, multi-party code-switching dialogues, including trilingual conversations. It offers greater structural diversity than machine-generated data. Evaluations show current language models struggle with code-switched inputs, emphasizing the need f...

🔹 Publication Date: Published on Jan 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17277
• PDF: https://arxiv.org/pdf/2601.17277

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#CodeSwitching #NLP #DialogueSystems #MultilingualAI #LLMs

✨Self-Improving Multilingual Long Reasoning via Translation-Reasoning Integrated Training

📝 Summary:
TRIT framework improves multilingual long reasoning by jointly training translation and reasoning. This self-improving method enhances non-English question understanding and response generation without extra data. It boosts accuracy and language consistency, also improving cross-lingual question ...

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05940
• PDF: https://arxiv.org/pdf/2602.05940

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#MultilingualAI #LongReasoning #LLM #NLP #AIResearch

✨What Really Controls Temporal Reasoning in Large Language Models: Tokenisation or Representation of Time?

📝 Summary:
MultiTempBench evaluates LLMs multilingual temporal reasoning across various calendars and languages. It finds that tokenization quality, specifically fragmentation of temporal data, is a major bottleneck that severely reduces accuracy in low-resource languages and less common calendar formats.

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.19017
• PDF: https://arxiv.org/pdf/2603.19017
• Github: https://github.com/gagan3012/mtb

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#LLM #TemporalReasoning #Tokenization #MultilingualAI #NLP

✨Language on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilinguality

📝 Summary:
XBridge combines LLMs with translation models to boost multilingual performance, especially for low-resource languages. It keeps the LLM as an English knowledge core, bridging model misalignment with lightweight mapping layers for semantic consistency without retraining the LLM.

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17512
• PDF: https://arxiv.org/pdf/2603.17512
• Github: https://github.com/ictnlp/XBridge

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#LLM #MultilingualAI #NLP #LowResourceLanguages #AIResearch

✨GlotOCR Bench: OCR Models Still Struggle Beyond a Handful of Unicode Scripts

📝 Summary:
Current OCR models poorly generalize across diverse scripts. GlotOCR Bench, a new benchmark for over 100 Unicode scripts, reveals most models perform well on under ten scripts. Generalization is limited and strongly depends on pretraining coverage.

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.12978
• PDF: https://arxiv.org/pdf/2604.12978
• Project Page: https://huggingface.co/datasets/cis-lmu/GlotOCR-bench
• Github: https://github.com/cisnlp/glotocr-bench

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#OCR #NLP #MultilingualAI #Benchmarking #AIResearch