Abstract: Methods for determining black-box representations of machine learning models when information pertaining to internal states or parameters of the models are not accessible are disclosed. By using outputs of the model instead of internal states, the black-box representation is model-agnostic and provides a reliable and robust representation of the model using an external lens. The black-box representation is generated using responses from the model to a series of initialization and elicitation questions that quantify the confidence that the model has in answers it just returned. The black-box representation is then used as a training dataset for a linear classifier in order to learn performance metrics about the model.

Abstract: A computer-implemented method that includes receiving a plurality of input images, generating a visual matrix utilizing the plurality of images and an image encoder, wherein the visual matrix includes a list of encoded images, receiving a plurality of text prompts, selecting a text prompt from the plurality of text prompts, send the first one of the text prompts to a language model to generate a candidate list of tokens, selecting tokens, converting the text prompts into updated text prompts via appending the tokens, generating a text matrix utilizing the text prompt and text encoder, and utilizing numerical values assigned at an image-text similarity matrix, determining a score associated with the image-text similarity matrix; and evaluating a criteria and outputting a final token to the updated text prompt in response to identifying a highest score associated with the final token after evaluating each of the plurality of text prompts.

Abstract: A computer-implemented method that includes receiving a plurality of input images, generating a visual matrix utilizing the plurality of images and an image encoder, wherein the visual matrix includes a list of encoded images, receiving a plurality of text prompts, selecting a text prompt from the plurality of text prompts, send the first one of the text prompts to a language model to generate a candidate list of tokens, selecting tokens, converting the text prompts into updated text prompts via appending the tokens, generating a text matrix utilizing the text prompt and text encoder, and utilizing numerical values assigned at an image-text similarity matrix, determining a score associated with the image-text similarity matrix; and evaluating a criteria and outputting a final token to the updated text prompt in response to identifying a highest score associated with the final token after evaluating each of the plurality of text prompts.