Assessing the quality of free text responses is challenging compared to traditional machine learning evaluations; it requires semantic comparison to align with human judgment. Unlike objective datasets, free text responses often lack clear evaluation metrics. To enhance model performance, investing in 'golden' datasets with reliable human-assigned grades is crucial. This investment addresses the bottleneck of continuous human evaluations and fosters collaboration between technical and domain experts to create applicable ground truths, while also emphasizing the importance of interpretability and ethical considerations in generating AI outputs.
Measuring the quality of free text responses is not trivial, requiring semantic comparisons to ensure alignment with human evaluation.
Creating 'golden' test datasets with statistically reliable human grades is essential for assessing the performance of language models effectively.
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