Using MemAlign to Improve Evaluation of Traditional Machine Learning in Genie Code
Summary
MemAlign, an open-source MLflow framework, significantly improved the evaluation of traditional machine learning in Genie Code by reducing LLM judge error by 74-89% on key dimensions. This alignment was achieved with ~50 labeled examples, demonstrating the importance of both semantic and episodic memory for closing the gap between LLM judges and human experts.
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