Six papers by CopeNLU authors have been accepted for presentation at ACL 2026, on topics including fact-checking and uncertainty, evaluation protocols for retrieval, factual consistency in long-document summarization, context utilisation in language models, and gender bias in LLM-generated persuasive text.
CUB: Benchmarking Context Utilisation Techniques for Language Models. Lovisa Hagström, Youna Kim, Haeun Yu, Sang-goo Lee, Richard Johansson, Hyunsoo Cho, Isabelle Augenstein.
Explaining Sources of Uncertainty in Automated Fact-Checking. Jingyi Sun, Greta Warren, Irina Shklovski, Isabelle Augenstein.
8 papers by CopeNLU authors are accepted to appear at EMNLP 2025, on topics including explainability and cross-cultural NLP.
Graph-Guided Textual Explanation Generation Framework. Shuzhou Yuan, Jingyi Sun, Michael Färber, Steffen Eger, Pepa Atanasova, Isabelle Augenstein.
Self-Critique and Refinement for Faithful Natural Language Explanations. Yingming Wang, Pepa Atanasova.
FLARE: Faithful Logic-Aided Reasoning and Exploration. Erik Arakelyan, Pasquale Minervini, Pat Verga, Patrick Lewis, Isabelle Augenstein.
Explainability and Interpretability of Multilingual Large Language Models: A Survey.
4 papers by CopeNLU authors are accepted to appear at ACL 2023. The papers make contributions within faithfulness of explanations, measuring intersectional biases, event extraction and few-shot stance detection.
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection. Erik Arakelyan, Arnav Arora, Isabelle Augenstein.
Faithfulness Tests for Natural Language Explanations. Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, Thomas Lukasiewicz, Jakob Grue Simonsen, Isabelle Augenstein.
Measuring Intersectional Biases in Historical Documents. Nadav Borenstein, Karolina Stańczak, Thea Rolskov, Natacha Klein Käfer, Natália da Silva Perez, Isabelle Augenstein.
Extract information about entities, phrases and relations between them from text to populate knowledge bases