Question Answering Model

Question answering (QA) models aim to automatically answer questions posed in natural language, focusing on retrieving relevant information and generating accurate responses. Current research emphasizes improving retrieval methods, particularly for complex questions requiring multi-hop reasoning and handling diverse knowledge sources, often leveraging large language models (LLMs) and graph-based approaches. These advancements are crucial for various applications, including biomedical literature analysis, financial document processing, and interactive AI systems, driving efforts to enhance model robustness, mitigate biases, and improve explainability.