Community Question Answering

Community Question Answering (CQA) focuses on automatically improving the efficiency and effectiveness of online question-and-answer platforms. Current research emphasizes enhancing answer selection and ranking using techniques like reinforcement learning from human feedback (RLHF), pre-trained language models (PLMs) such as BERT and GPT, and deep learning architectures incorporating features from question metadata and user interactions. These advancements aim to address challenges like information overload, diverse user preferences, and the need for context-aware evaluation, ultimately improving user experience and facilitating knowledge discovery on CQA platforms.