AI Training for Mathematics
Interacted with AI-generated mathematical reasoning to check for logical correctness, consistency with facts, and soundness of the analyses with the goal to raise the level of the model's performance. Brought into being and maintained well-defined math-related datasets by writing new problems, their solutions, and explanations for model fine-tuning. Compared and rated the different AI outputs in terms of quantitative and qualitative metrics in order to enhance the quality of the responses. Contributed to the provision of the feedback and the issuance of the corrective inputs in order to upgrade the model interpretability, reasoning accuracy, and prompt design. Used cutting-edge statistical and mathematical methods to confirm and refine the training data for large language models. Kept consistent standards for data labeling and made sure that the documentation was at a level that allowed the model evaluation processes to be reproducible.