Yazar "Bayram, M. Ali" seçeneğine göre listele

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    Healthcare-Focused Turkish Medical LLM: Training on Real Patient-Doctor Question-Answer Data for Enhanced Medical Insight
    (Assoc Computing Machinery, 2025) Bayram, M. Ali; Diri, Banu; Yildirim, Savas
    The development of a Turkish-specific Large Language Model (LLM) for healthcare presents a unique opportunity to enhance AI's accessibility and relevance for Turkish-speaking medical practitioners and patients. This study introduces a specialized Turkish Medical LLM fine-tuned on over 167,732 real patient-doctor question-answer pairs sourced from a trusted medical platform and capturing authentic linguistics in Turkish medical language. Utilizing models like LLAMA 3, the fine-tuning process was supported by Low-Rank Adaptation (LoRA) and involved innovative methods to mitigate catastrophic forgetting, including spherical linear interpolation (Slerp) merging. Evaluation of the model's performance through similarity scores, GPT-3.5 assessments, and expert reviews indicates significant improvement in the model's ability to generate medically accurate responses. This Turkish Medical LLM demonstrates potential to support medical decision-making and patient interaction in Turkish healthcare settings, offering an essential resource for enhancing AI inclusivity across languages.
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    Tokenization Standards and Evaluation in Natural Language Processing: A Comparative Analysis of Large Language Models on Turkish
    (Ieee, 2025) Bayram, M. Ali; Fincan, Ali Arda; Gumus, Ahmet Semih; Karakas, Sercan; Diri, Banu; Yildirim, Savas
    Tokenization is a fundamental preprocessing step in Natural Language Processing (NLP), significantly impacting the capability of large language models (LLMs) to capture linguistic and semantic nuances. This study introduces a novel evaluation framework addressing tokenization challenges specific to morphologically-rich and low-resource languages such as Turkish. Utilizing the Turkish MMLU (TR-MMLU) dataset, comprising 6,200 multiple-choice questions from the Turkish education system, we assessed tokenizers based on vocabulary size, token count, processing time, language-specific token percentages (%TR), and token purity (%Pure). These newly proposed metrics measure how effectively tokenizers preserve linguistic structures. Our analysis reveals that language-specific token percentages exhibit a stronger correlation with downstream performance (e.g., MMLU scores) than token purity. Furthermore, increasing model parameters alone does not necessarily enhance linguistic performance, underscoring the importance of tailored, language-specific tokenization methods. The proposed framework establishes robust and practical tokenization standards for morphologically complex languages.
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    TR-MMLU Benchmark for Large Language Models: Performance Evaluation, Challenges, and Opportunities for Improvement
    (Ieee, 2025) Bayram, M. Ali; Fincan, Ali Arda; Gumus, Ahmet Semih; Diri, Banu; Yildirim, Savas; Aytas, Oner
    Language models have made significant advancements in understanding and generating human language, achieving remarkable success in various applications. However, evaluating these models remains a challenge, particularly for resource-limited languages like Turkish. To address this issue, we introduce the Turkish MMLU (TR-MMLU) benchmark, a comprehensive evaluation framework designed to assess the linguistic and conceptual capabilities of large language models (LLMs) in Turkish. TR-MMLU is based on a meticulously curated dataset comprising 6,200 multiple-choice questions across 62 sections within the Turkish education system. This benchmark provides a standard framework for Turkish NLP research, enabling detailed analyses of LLMs' capabilities in processing Turkish text. In this study, we evaluated state-of-the-art LLMs on TR-MMLU, highlighting areas for improvement in model design. TR-MMLU sets a new standard for advancing Turkish NLP research and inspiring future innovations.