Chain of Draft: Thinking Faster by Writing Less

Silei Xu, Wenhao Xie, Lingxiao Zhao, Pengcheng He

Chain of Draft: Thinking Faster by Writing Less: 48 upvotes on Hugging Face Daily Papers, #2 of 18 papers on 2025-03-03. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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