Chain-of-Draft: The End of Chain of Thought? (Faster Results, Less Cost!)

1. Chain of Draft offers a more efficient prompting strategy.

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Chain of Draft mimics human thinking by generating concise outputs, reducing latency and cost compared to Chain of Thought.

• It allows models to focus on essential information without verbose reasoning.
• This method can lead to faster results while maintaining accuracy.
• Chain of Draft is on par or exceeds Chain of Thought in performance.

2. Chain of Thought has inherent limitations.

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While effective, Chain of Thought requires substantial computational resources and leads to verbose outputs, increasing latency.

• It breaks down problems step by step, which can be resource-intensive.
• The verbosity contrasts with how humans typically solve problems.
• Overthinking simple tasks can lead to unnecessary resource consumption.

3. Implementing Chain of Draft is straightforward.

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No model updates or fine-tuning are needed; simply adjust the prompt to utilize Chain of Draft.

• The prompt can guide the model to keep responses concise.
• A word limit can be set to ensure brevity in outputs.
• This approach enhances efficiency without complex changes.

4. Performance metrics favor Chain of Draft.

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Chain of Draft achieves high accuracy with significantly fewer tokens and lower latency than Chain of Thought.

• For example, it used only 43 tokens compared to 200 for Chain of Thought.
• Latency was reduced to one second versus 4.2 seconds.
• This efficiency is crucial for applications requiring numerous iterations.

5. Chain of Draft can outperform Chain of Thought in specific tasks.

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In some benchmarks, Chain of Draft exceeded the performance of Chain of Thought while using fewer resources.

• For common sense reasoning, Chain of Draft was nearly identical in accuracy but used less than half the tokens.
• In sports understanding tasks, it outperformed Chain of Thought in both models tested.
• This highlights the potential for cost-effective AI solutions.