Generative artificial intelligence (AI) systems are trained on large data corpora to generate
new pieces of text, images, videos, and other media. There is growing concern that such …

A multimodal large language model (MLLM) agent can receive instructions, capture images,
retrieve histories from memory, and decide which tools to use. Nonetheless, red-teaming …

While Explainable AI (XAI) aims to make AI understandable and useful to humans, it has
been criticised for relying too much on formalism and solutionism, focusing more on …

How does the internal computation of a machine learning model transform inputs into
predictions? In this paper, we introduce a task called component modeling that aims to …

How can we attribute the behaviors of machine learning models to their training data? While
the classic influence function sheds light on the impact of individual samples, it often fails to …

Deep generative models have demonstrated impressive performance in various computer
vision applications, including image synthesis, video generation, and medical analysis …

Diffusion models have led to significant advancements in generative modelling. Yet their
widespread adoption poses challenges regarding data attribution and interpretability. In this …

As diffusion models are deployed in real-world settings, data attribution is needed to ensure
fair acknowledgment for contributors of high-quality training data and to identify sources of …

The goal of data attribution for text-to-image models is to identify the training images that
most influence the generation of a new image. We can define" influence" by saying that, for a …

Diffusion models, which revolutionized image generation, are facing challenges related to
intellectual property. These challenges arise when a generated image is influenced by …