12 papers
Efficient Continual Learning for Small Language Models with a Discrete Key-Value Bottleneck
Andor Diera, Lukas Galke, Fabian Karl +1
Continual learning remains a challenge across various natural language processing (NLP) tasks, as models updated with new training data often risk catastrophic forgetting of previo…
Your Extreme Multi-label Classifier is Secretly a Hierarchical Text Classifier for Free
Nerijus Bertalis, Paul Granse, Ferhat Gül +6
Assigning a set of labels to a given text is a classification problem with many real-world applications, such as recommender systems. Two separate research streams address this iss…
Not Everything That Counts Can Be Counted: A Case for Safe Qualitative AI
Stine Beltoft, Lukas Galke
Artificial intelligence (AI) and large language models (LLM) are reshaping science, with most recent advances culminating in fully-automated scientific discovery pipelines. But qua…
Gumbel-MPNN: Graph Rewiring with Gumbel-Softmax
Marcel Hoffmann, Lukas Galke, Ansgar Scherp
Graph homophily has been considered an essential property for message-passing neural networks (MPNN) in node classification. Recent findings suggest that performance is more closel…
Four Shades of Life Sciences: A Dataset for Disinformation Detection in the Life Sciences
Eva Seidlmayer, Lukas Galke, Konrad U. Förstner
Disseminators of disinformation often seek to attract attention or evoke emotions - typically to gain influence or generate revenue - resulting in distinctive rhetorical patterns t…
A Transformer-based Autoregressive Decoder Architecture for Hierarchical Text Classification
Younes Yousef, Lukas Galke, Ansgar Scherp
Recent approaches in hierarchical text classification (HTC) rely on the capabilities of a pre-trained transformer model and exploit the label semantics and a graph encoder for the…