Question Answering Framework

Question answering (QA) frameworks aim to build systems that accurately and efficiently answer questions posed in natural language. Current research focuses on improving the accuracy and adaptability of these frameworks, particularly using large language models (LLMs) augmented with techniques like Retrieval Augmented Generation (RAG) and active learning to address limitations such as hallucinations and context misinterpretations. These advancements are driven by the need for robust and versatile QA systems across diverse applications, from customer service support to scientific literature analysis and AI education, ultimately impacting fields requiring efficient information retrieval and knowledge synthesis.