o1-preview brings built-in reasoning to medical diagnosis, outperforming traditional prompting methods.
OpenAI's o1-preview model demonstrates superior performance on medical challenges without complex prompting techniques, outperforming GPT-4 with Medprompt across various benchmarks.
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https://arxiv.org/abs/2411.03590
Original Problem 🤔:
→ Traditional LLMs require extensive prompt engineering and run-time strategies like Medprompt to perform well on specialized medical tasks. This increases complexity and computational costs.
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Solution in this Paper 🔧:
→ The paper evaluates OpenAI's o1-preview model, which has built-in reasoning capabilities through reinforcement learning training.
→ The model inherently performs step-by-step problem-solving during inference without needing explicit chain-of-thought prompting.
→ It dynamically allocates computational resources during inference for better results on challenging problems.
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Key Insights 💡:
→ Few-shot prompting actually hinders o1-preview's performance, suggesting in-context learning isn't effective for reasoning-native models
→ Ensembling remains valuable but requires careful cost-performance optimization
→ The model achieves near-saturation on existing medical benchmarks, indicating need for more challenging evaluation metrics
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Results 📊:
→ o1-preview scored 96.0% on MedQA vs 90.2% for GPT-4 with Medprompt
→ Achieved 98.2% accuracy on JMLE-2024, a new Japanese medical licensing exam benchmark
→ Performance scales with reasoning tokens - more reasoning time leads to better accuracy
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