[Paper Reading] Chain of Draft: Thinking Faster by Writing Less

​This week, we will walk through and discuss the paper:

​Chain of Draft: Thinking Faster by Writing Less

​[https://arxiv.org/abs/2502.18600]

​Abstract of the paper:

​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.

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​We are a group of applied AI practitioners and enthusiasts who have formed a collective learning community. Every Wednesday evening at PM PST, we hold our research paper reading seminar covering an AI topic. One member carefully explains the paper, making it more accessible to a broader audience. Then, we follow this reading with a more informal discussion and socializing.

​Today's paper reading session on March 5 will be held via Zoom only. There will be no in-person meeting.

​Zoom Link to Join the Paper Reading session - https://us02web.zoom.us/meeting/register/tZUvf-uvrTwvHdP9B-vE03j3BapgRypn64CS