Showing 2024 · cs.CLShow all
3 papers · 2 filters
cs.CL2024
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…
cs.CL2024
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…
cs.CL2024
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…