Natural Language Answer

Natural language answer generation focuses on creating systems that respond to questions using free-form text, drawn from various sources like text passages, images, or audio. Current research emphasizes improving accuracy and efficiency across diverse modalities, including visual question answering (VQA) and audio question answering (AQA), often leveraging transformer-based models like LLMs and attention mechanisms to process and integrate information from multiple sources. These advancements have significant implications for fields like education (automating assessment), e-commerce (providing automated customer service), and healthcare (generating medical reports), improving efficiency and accessibility.