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docs/_posts/2024-7-25-socratic-llm.md

@@ -54,15 +54,15 @@ We fine-tuned three models over three different datasets ([Debugging](https://ar
 We observe that the model trained on TutorChat is the most
 performing, yielding good performance on all three datasets. Notably, the TutorChat-trained model surpasses the models trained on MathDial and Debugging when evaluated on their respective test sets, albeit by a small margin. Such an effect is likely due to the preference dataset of TutorChat, which indicates a higher data diversity than the MathDial and Debugging datasets.
 
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 Below, we present the mean summary scores over the 100 samples for the TutorChat fine-tuned model and the base model using only prompt engineering. We add GPT-4o's performance with only prompt engineering to provide a reference of the best possible performance with prompt engineering-only strategies. The fine-tuned model improved significantly over the base model, reaching close performance to a much larger and more powerful GPT-4o in all datasets.
 
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 The TutorChat-trained model (our best model) showed significant gains in three key areas and now performs almost as well as GPT-4o. This also shows the model's strong generalization ability, as it was trained on TutorChat data but excelled on the different MathDial datasets.
 
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 It also showed significant gains in three areas, nearing GPT-4o performance, and demonstrated strong generalization by excelling on a dataset different from its training data.