Generative AI presents a progressive approach to leveraging the value of unstructured data for industrial use.
can help manage this issue by assessing the model's ability to produce outputs supported by given context or input data and identifying specific components where hallucinations may occur. Additionally, reliable and diverse internal data sets can significantly upgrade the reliability of AI-generated outputs. Model regularization penalizes AI behaviors that deviate from company norms, ensuring consistent outputs.
To ensure consistent and effective generative AI performance, implementing key evaluation metrics is a crucial step. For instance, in language modeling, measures such as perplexity and cross-entropy loss provide insights into the model's proficiency in predicting the probability distribution of test data, and the divergence between its predicted and true distributions respectively.
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