title: Chain of Draft: Thinking Faster by Writing Less
summary: 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_authors._id: 67c0a8058589d8ecb79d472b
paper_authors.name: Silei Xu
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-27T18:01:14.543Z
paper_authors._id: 6594b1bb57a556fbe162915e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6594b1bb57a556fbe162915e/WuYxqbbvaJaT-xsk5KhoT.jpeg
paper_authors.fullname: Silei Xu
paper_authors.type: user
paper_authors.user: sileixu
paper_authors._id: 67c0a8058589d8ecb79d472c
paper_authors.name: Wenhao Xie
paper_authors._id: 67c0a8058589d8ecb79d472d
paper_authors.name: Lingxiao Zhao
paper_authors._id: 67c0a8058589d8ecb79d472e
paper_authors.name: Pengcheng He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-03T09:30:43.479Z
paper_authors._id: 5efd09cf49ed724c8a135868
paper_authors.avatarUrl: /avatars/af12bc94657979677a9f26183f0c9727.svg
paper_authors.fullname: Pengcheng He
paper_authors.type: user
paper_authors.user: DeBERTa
paper_title: Chain of Draft: Thinking Faster by Writing Less
paper_summary: 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_ai_keywords: Chain-of-Thought (CoT) verbose step-by-step reasoning Chain of Draft (CoD) cognitive processes minimalistic informative intermediate outputs critical insights CoD accuracy tokens cost latency downstream reasoning tasks