Large foundation models are like black boxes! We regularly hear this statement associated with the limited interpretability in the current generation of large generative AI models across different modalities. But what really makes these models so difficult to interpret' Is it just size or there are other more intrinsic complexities' The advent of large foundation models has revolutionized the field of artificial intelligence, bringing unprecedented capabilities in natural language processing, image generation, and multi-modal tasks. However, these models present significant challenges in terms of interpretability, far surpassing those encountered in traditional machine learning approaches. This essay explores the landscape of interpretability for large foundation models, examining the limitations of conventional techniques and delving into emerging fields that aim to shed light on the inner workings of these complex systems.
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