Side Chain

"Chain of thought" (CoT) is a prompting technique used to improve the reasoning abilities of large language models (LLMs) by decomposing complex problems into a series of intermediate steps. Current research focuses on enhancing CoT's effectiveness through various methods, including algorithmic improvements (e.g., bidirectional chaining, tree-based search), model architectures (e.g., incorporating CoT into diffusion models, multi-agent systems), and data augmentation (e.g., generating diverse reasoning chains for self-correction). This research is significant because it addresses limitations in LLMs' ability to solve complex problems and has implications for various applications, including improved natural language processing, code generation, and medical diagnosis.