Complex Question

Complex question answering (CQA) focuses on developing systems capable of understanding and responding to questions requiring multi-step reasoning and diverse knowledge sources. Current research emphasizes improving large language models (LLMs) through techniques like question decomposition, intermediate representation generation, and hybrid architectures combining LLMs with symbolic reasoning methods, often leveraging knowledge graphs or textual databases. These advancements aim to enhance the accuracy and explainability of CQA systems, with implications for various applications including education, scientific research, and information retrieval. The ultimate goal is to create robust and reliable systems that can handle the nuances and complexities of human language.