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Svetlana Maslenkova

2 accepted papers

2025

Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency

EMNLP 2025

Large language models offer transformative potential for healthcare, yet their responsible and equitable development depends critically on a deeper understanding of how training data characteristics influence model behavior, including the potential for bias. Current practices in dataset curation and

2024

Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs.

EMNLP 2024finding

Large Language Models (LLMs) have demonstrated significant potential in revolutionizing clinical applications. In this study, we investigate the efficacy of four techniques in adapting LLMs for clinical use-cases: continuous pretraining, instruct fine-tuning, NEFTune, and prompt engineering. We empl…

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