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20232025
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cs.CL2025

Identifying Fine-grained Forms of Populism in Political Discourse: A Case Study on Donald Trump's Presidential Campaigns

Ilias Chalkidis, Stephanie Brandl, Paris Aslanidis

Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of instruction-following tasks, yet their grasp of nuanced social science concepts remain…

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

Llama meets EU: Investigating the European Political Spectrum through the Lens of LLMs

Ilias Chalkidis, Stephanie Brandl

Instruction-finetuned Large Language Models inherit clear political leanings that have been shown to influence downstream task performance. We expand this line of research beyond t…

cs.CL2023

On the Interplay between Fairness and Explainability

Stephanie Brandl, Emanuele Bugliarello, Ilias Chalkidis

In order to build reliable and trustworthy NLP applications, models need to be both fair across different demographics and explainable. Usually these two objectives, fairness and e…

cs.CL2023

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…