Complex Question Answering

Complex Question Answering (CQA) focuses on enabling computers to understand and answer questions requiring multi-step reasoning and information retrieval from diverse sources. Current research emphasizes improving large language models (LLMs) for CQA by incorporating techniques like step-wise planning guided by knowledge graphs, and developing better methods for evaluating the accuracy and attribution of LLM-generated answers, often using fine-grained categorization schemes. These advancements are crucial for building more reliable and trustworthy AI systems capable of handling nuanced information needs in various applications, from scientific research to everyday information access.