QA System

Question answering (QA) systems aim to automatically provide accurate and informative answers to user queries, a goal pursued through various architectures including retrieval-augmented models combining large language models (LLMs) with information retrieval techniques and DeBERTa-based models for specialized domains. Current research emphasizes improving evaluation methods, addressing biases in datasets, enhancing model fluency and reliability through feedback loops, and mitigating user over-reliance on potentially inaccurate answers by providing relevant background information. These advancements are crucial for building trustworthy and effective QA systems across diverse applications, from healthcare to e-governance and defense.