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Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study
This paper investigates the impact of domain-specific model fine-tuning and of reasoning mechanisms on the performance of question-answering (Q&A) systems powered by large language models (LLMs) and Retrieval-Augmented Generation (RAG).
Addressing catastrophic forgetting for medical domain expansion
Notably, this approach degrades model performance at the original institution, a phenomenon known as catastrophic forgetting.
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