Text Answer

Text answer generation research focuses on developing systems that accurately and comprehensively answer questions using textual information, often incorporating multimodal data or leveraging knowledge graphs to enhance accuracy and address limitations of relying solely on language models. Current research emphasizes improving the coherence and usefulness of generated answers, mitigating issues like clickbait and addressing biases in automated grading systems, while also exploring efficient model training and evaluation methods. These advancements have significant implications for various applications, including customer service chatbots, educational tools, and automated assessment systems, ultimately aiming to improve human-computer interaction and information access.