68 citations · 75 across the 11 of their papers we have counts for
7 papers · 1 filter
SENSESHIFT: Continuous Sentiment-Controlled Text Generation via Encoder-based Mask Infilling
Shahed Masoudian, Markus Frohmann, Emmanouil Karystinaios +2
Recent controllable text generation (CTG) for sentiment control has largely focused on decoder-based large language models, making causal attention the dominant paradigm. While eff…
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization
Shahed Masoudian, Markus Frohmann, Navid Rekabsaz +1
Language models frequently inherit societal biases from their training data. Numerous techniques have been proposed to mitigate these biases during both the pre-training and fine-t…
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition
Carolin Holtermann, Markus Frohmann, Navid Rekabsaz +1
The knowledge encapsulated in a model is the core factor determining its final performance on downstream tasks. Much research in NLP has focused on efficient methods for storing an…
Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification
Nicolò Tamagnone, Selim Fekih, Ximena Contla +2
Accurate and rapid situation analysis during humanitarian crises is critical to delivering humanitarian aid efficiently and is fundamental to humanitarian imperatives and the Leave…
Parameter-efficient Modularised Bias Mitigation via AdapterFusion
Deepak Kumar, Oleg Lesota, George Zerveas +4
Large pre-trained language models contain societal biases and carry along these biases to downstream tasks. Current in-processing bias mitigation approaches (like adversarial train…
HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response
Selim Fekih, Nicolò Tamagnone, Benjamin Minixhofer +4
Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP…