Intrinsic Evaluation

Intrinsic evaluation assesses the internal qualities of machine learning models, focusing on understanding their learned representations and knowledge rather than solely on downstream task performance. Current research emphasizes developing novel metrics that capture nuanced aspects of model behavior, such as the representation of specific concepts within model parameters or the alignment of learned representations with human judgments of similarity. This shift towards more comprehensive intrinsic evaluation is crucial for improving model transparency, identifying and mitigating biases, and ultimately guiding the development of more robust and reliable AI systems across various applications.