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This work introduces EAR, an Entity-Aware Partitioning approach for multiple-choice question answering (MCQA), which provides a compact and inspectable retrieval unit, while its rule-based anchor extractor remains domain-specific and requires separate validation before transfer.
Large language models have high computation and inference costs. Recently, Small Language Models (SLMs) have become more important because they require fewer resources and offer high efficiency. Different training approaches can be used for SLMs to achieve high performance, even on resource-constrained hardware.In this...
This study investigates the English-to-Turkish clinical translation performance of three large language models (Aya-23, Seed-X-Instruct, Gemma-3) using zero/few-shot prompting and QLoRA fine-tuning. Our three-stage setup evaluates: (1) general translation after FLORES-devtest training, (2) medical domain adaptation on...
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