Chain of Draft: Thinking Faster by Writing Less
Authors: name: Silei Xu, name: Wenhao Xie, name: Lingxiao Zhao, name: Pengcheng He
Organization: Zoom AI

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.