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@@ -60,7 +60,11 @@ Additional information about the datasets will be included in the Meditron-3 pub
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  #### Evaluation
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- Evaluation results for the Gemma[2]-Meditron-3[2B] are coming soon!
 
 
 
 
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  We evaluated Meditron on medical multiple-choice questions using [lm-harness](https://github.com/EleutherAI/lm-evaluation-harness) for reproducibility.
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  While MCQs are valuable for assessing exam-like performance, they fall short of capturing the model's real-world utility, especially in terms of contextual adaptation in under-represented settings. Medicine is not multiple choice and we need to go beyond accuracy to assess finer-grained issues like empathy, alignment to local guidelines, structure, completeness and safety. To address this, we have developed a platform to collect feedback directly from experts to continuously adapt to the changing contexts of clinical practice.
 
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  #### Evaluation
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+ | Model Name | MedmcQA | MedQA | PubmedQA | Average |
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+ |-----------------------------|---------|--------|----------|---------|
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+ | google/gemma-2-2b | 40.31 | 34.80 | 74.20 | 49.77 |
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+ | gemMeditron-2-2b-it | 42.51 | 38.81 | 75.40 | 52.24 |
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+ | Difference (gemMeditron vs.)| 2.20 | 4.01 | 1.20 | 2.47 |
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  We evaluated Meditron on medical multiple-choice questions using [lm-harness](https://github.com/EleutherAI/lm-evaluation-harness) for reproducibility.
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  While MCQs are valuable for assessing exam-like performance, they fall short of capturing the model's real-world utility, especially in terms of contextual adaptation in under-represented settings. Medicine is not multiple choice and we need to go beyond accuracy to assess finer-grained issues like empathy, alignment to local guidelines, structure, completeness and safety. To address this, we have developed a platform to collect feedback directly from experts to continuously adapt to the changing contexts of clinical practice.