Paper · arXiv 2502.18600
Chain of Draft: Thinking Faster by Writing Less
Silei Xu, Wenhao Xie, Lingxiao Zhao, Pengcheng He
Chain of Draft (CoD) improves the efficiency of Large Language Models (LLMs) in reasoning tasks by generating concise intermediate thoughts, enhancing accuracy while reducing token usage, cost, and latency.
Abstract
Large Language Models (LLMs) have demonstrated remarkable performance in solving complex reasoning tasks through mechanisms like Chain-of-Thought (CoT) prompting, which emphasizes verbose, step-by-step reasoning. However, humans typically employ a more efficient strategy: drafting concise intermediate thoughts that capture only essential information. In this work, we propose Chain of Draft (CoD), a novel paradigm inspired by human cognitive processes, where LLMs generate minimalistic yet informative intermediate reasoning outputs while solving tasks. By reducing verbosity and focusing on critical insights, CoD matches or surpasses CoT in accuracy while using as little as only 7.6% of the tokens, significantly reducing cost and latency across various reasoning tasks.