6 papers · 1 filter
Trick or Neat: Adversarial Ambiguity and Language Model Evaluation
Antonia Karamolegkou, Oliver Eberle, Phillip Rust +2
Detecting ambiguity is important for language understanding, including uncertainty estimation, humour detection, and processing garden path sentences. We assess language models' se…
A Systematic Comparison between Extractive Self-Explanations and Human Rationales in Text Classification
Stephanie Brandl, Oliver Eberle
Instruction-tuned LLMs are able to provide \textit{an} explanation about their output to users by generating self-explanations, without requiring the application of complex interpr…
Explaining Text Similarity in Transformer Models
Alexandros Vasileiou, Oliver Eberle
As Transformers have become state-of-the-art models for natural language processing (NLP) tasks, the need to understand and explain their predictions is increasingly apparent. Espe…
Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations
Stephanie Brandl, Oliver Eberle, Tiago Ribeiro +2
Rationales in the form of manually annotated input spans usually serve as ground truth when evaluating explainability methods in NLP. They are, however, time-consuming and often bi…
Rather a Nurse than a Physician -- Contrastive Explanations under Investigation
Oliver Eberle, Ilias Chalkidis, Laura Cabello +1
Contrastive explanations, where one decision is explained in contrast to another, are supposed to be closer to how humans explain a decision than non-contrastive explanations, wher…
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?
Stephanie Brandl, Oliver Eberle, Jonas Pilot +1
Learned self-attention functions in state-of-the-art NLP models often correlate with human attention. We investigate whether self-attention in large-scale pre-trained language mode…