Microsoft’s new Medprompt technique boosts GPT-4 to edge out Google’s Gemini Ultra on MMLU benchmark tests by a narrow margin. The technique involves dynamic few-shot learning, self-generated chain of thought prompting, and choice shuffle ensembling, proving older AI models can surpass expectations when prompted cleverly. The approach offers exciting possibilities but may require additional processing power.
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Microsoft shades Gemini with GPT-4 boosted by Medprompt
Earlier this month, Microsoft’s new prompting technique helped GPT-4 beat Google’s Gemini model on the MMLU benchmark tests, regaining the top spot with only a slight margin. This demonstrates that with proper prompting, older AI models can outperform newer ones.
Medprompt
Microsoft’s Medprompt project utilizes clever prompting techniques to guide AI models in providing better-aligned outputs. The combination of prompting techniques, including Dynamic Few-Shot Learning, Self-Generated Chain of Thought, and Choice Shuffle Ensembling, has proven to be effective in improving the performance of GPT-4 on a range of tasks, including specialist medical tests and generalist benchmarks.
Medprompt improvements on MedQA test performance. Microsoft
How does Medprompt work?
Medprompt leverages three main techniques:
- Dynamic Few-Shot Learning (DFSL): This involves providing the AI model with a few examples relevant to the task at hand, allowing it to make more specific and accurate predictions.
- Self-Generated Chain of Thought (CoT): By guiding the model’s chain of thought through careful prompting, the results are significantly improved.
- Choice Shuffle Ensembling: This technique addresses positional bias in multiple-choice questions by shuffling answer options and selecting the most consistently chosen response.
Combining these prompt techniques has given Microsoft a competitive edge, highlighting the potential for older AI models to achieve impressive performance improvements through strategic prompting.
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