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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · arXiv cs.AI English(EN) ·

    Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization

    Researchers have developed a novel LLM-based architecture to enhance the accuracy of French clinical interview transcriptions and speaker identification. This multi-pass system alternates between speaker and word recognition passes, demonstrating significant reductions in Word Error Rate (WER) on suicide prevention conversations. The approach, tested using the Qwen3-Next-80B model, showed feasibility for offline clinical deployment with an acceptable real-time factor of 0.32. AI

    IMPACT Introduces a specialized LLM application for improving clinical transcription accuracy, potentially aiding medical professionals.

  2. RESEARCH · arXiv cs.CL English(EN) · · [2 sources]

    Do LLMs Know What Luxembourgish Borrows? Probing Lexical Neology in Low-Resource Multilingual Models

    Researchers have developed a new benchmark, LexNeo-Bench, to evaluate how well large language models understand lexical borrowing in low-resource languages like Luxembourgish. The benchmark, derived from a Luxembourgish news corpus, labels tokens as native or borrowed from French, German, or English. When prompted with a linguistic knowledge graph, LLMs showed significantly improved accuracy in classifying borrowed words, narrowing the performance gap between smaller and larger models. AI

    IMPACT Enhances LLM evaluation for low-resource languages, potentially improving writing assistance tools for diverse linguistic communities.

  3. RESEARCH · arXiv cs.CL English(EN) · · [9 sources]

    Translate or Simplify First: An Analysis of Cross-lingual Text Simplification in English and French

    Researchers are exploring how large language models (LLMs) align with human brain activity across different languages and tasks. Studies show that intermediate LLM layers best predict brain responses, and this alignment is influenced by training data language dominance rather than inherent model typology. Furthermore, instruction-tuned multimodal LLMs demonstrate stronger brain alignment, particularly when organized around task-specific demands rather than just surface semantics. AI

    Translate or Simplify First: An Analysis of Cross-lingual Text Simplification in English and French

    IMPACT Investigates how LLMs process and represent information, offering insights into their cognitive alignment and potential for cross-lingual and multimodal tasks.