Question Decomposition

Question decomposition is a technique used to break down complex questions into simpler sub-questions, improving the performance of large language models (LLMs) on question-answering tasks. Current research focuses on developing methods for automatically decomposing questions, particularly for multi-hop reasoning and multimodal scenarios involving both text and images, often employing techniques like in-context learning and reinforcement learning within various model architectures including generative and modular approaches. This research is significant because it enhances the accuracy and interpretability of LLMs, leading to more reliable and explainable question-answering systems with applications in diverse fields like database querying and knowledge base access.