Chain of X

Chain-of-X (CoX) methods enhance the reasoning capabilities of large language models (LLMs) by structuring the reasoning process as a sequence of intermediate steps, where "X" represents the type of intermediate step. Current research focuses on adapting CoX to various tasks, including medical error correction, multi-turn text-to-SQL generation, vision-language understanding, and improving the robustness of retrieval-augmented LLMs. These advancements improve the accuracy and reliability of LLMs across diverse domains, leading to more effective and trustworthy applications in fields like healthcare, database management, and multimodal information processing.