Given that you're using cosine similarity of text embeddings to approximate the influence of individual tokens in a prompt, how does this approach fare in capturing higher-order interactions between tokens, something that Integrated Gradients (allegedly) is designed to account for? Are there specific scenarios where the cosine similarity method might fall short in capturing the nuances that Integrated Gradients can reveal?
Given that you're using cosine similarity of text embeddings to approximate the influence of individual tokens in a prompt, how does this approach fare in capturing higher-order interactions between tokens, something that Integrated Gradients (allegedly) is designed to account for? Are there specific scenarios where the cosine similarity method might fall short in capturing the nuances that Integrated Gradients can reveal?