5 papers
Teaching and Learning under Deductive Errors
Jan Arne Telle, Brigt HÃ¥vardstun, Jose Hernandez-Orallo
Most models of machine teaching and learning assume the learner makes no errors in its internal deductive inference. However, humans and large language models in few-shot learning…
Evaluating Simplification Algorithms for Interpretability of Time Series Classification
Brigt Håvardstun, Felix Marti-Perez, Cèsar Ferri +1
In this work, we introduce metrics to evaluate the use of simplified time series in the context of interpretability of a TSC -- a Time Series Classifier. Such simplifications are i…
Relative Drawing Identification Complexity is Invariant to Modality in Vision-Language Models
Diogo Freitas, Brigt Håvardstun, Cèsar Ferri +3
Large language models have become multimodal, and many of them are said to integrate their modalities using common representations. If this were true, a drawing of a car as an imag…
Can adversarial attacks by large language models be attributed?
Manuel Cebrian, Andres Abeliuk, Jan Arne Telle
Attributing outputs from Large Language Models (LLMs) in adversarial settings-such as cyberattacks and disinformation campaigns-presents significant challenges that are likely to g…
The Hierarchy of Saturating Matching Numbers
Hans U. Simon, Jan Arne Telle
In this paper, we study three matching problems all of which came up quite recently in the field of machine teaching. The cost of a matching is defined in such a way that, for some…