Generative Question Answering

Generative Question Answering (GQA) aims to build systems that can answer questions using natural language, often leveraging large language models (LLMs). Current research focuses on improving accuracy and reliability by addressing issues like hallucinations (fabricated answers), enhancing retrieval of relevant information from external knowledge bases (e.g., using vector databases and blended retrieval strategies), and improving confidence calibration through techniques like multi-agent deliberation. These advancements are significant for building more trustworthy and robust question-answering systems with applications across diverse fields, including safety engineering, legal tech, and education.