Chain of Draft: Thinking Faster by Writing Less
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.
67c0a8058589d8ecb79d472b
Silei Xu
extracted_confirmed
2025-02-27T18:01:14.543Z
6594b1bb57a556fbe162915e
https://cdn-avatars.huggingface.co/v1/production/uploads/6594b1bb57a556fbe162915e/WuYxqbbvaJaT-xsk5KhoT.jpeg
Silei Xu
user
sileixu
67c0a8058589d8ecb79d472c
Wenhao Xie
67c0a8058589d8ecb79d472d
Lingxiao Zhao
67c0a8058589d8ecb79d472e
Pengcheng He
admin_assigned
2025-03-03T09:30:43.479Z
5efd09cf49ed724c8a135868
/avatars/af12bc94657979677a9f26183f0c9727.svg
Pengcheng He
user
DeBERTa
Chain of Draft: Thinking Faster by Writing Less
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.
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.
Chain-of-Thought (CoT) Chain of Draft (CoD) Large Language Models (LLMs) reasoning tasks intermediate reasoning outputs critical insights