How do language models use information provided as context when generating a
response? Can we infer whether a particular generated statement is actually grounded in …

The rapid progress of research aimed at interpreting the inner workings of advanced
language models has highlighted a need for contextualizing the insights gained from years …

As businesses, products, and services spring up around large language models, the
trustworthiness of these models hinges on the verifiability of their outputs. However, methods …

Concept Bottleneck Models (CBMs) have garnered much attention for their ability to
elucidate the prediction process through a human-understandable concept layer. However …

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 …

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a
transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …

We demonstrate that a wide array of machine learning algorithms are specific instances of
one single paradigm: reciprocal learning. These instances range from active learning over …

Large language models (LLMs) are trained on a vast amount of human-written data, but data
providers often remain uncredited. In response to this issue, data valuation (or data …

In the current landscape of foundation model training, there is a significant reliance on public
domain data, which is nearing exhaustion according to recent research. To further scale up …

Influence functions aim to quantify the impact of individual training data points on a model's
predictions. While extensive research has been conducted on influence functions in …