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CopeNLU

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Natural Language Processing Section, Department of Computer Science, University of Copenhagen

CopeNLU is a Natural Language Processing research group led by Isabelle Augenstein and Pepa Atanasova with a focus on researching methods for tasks that require a deep understanding of language, as opposed to shallow processing. We are affiliated with the Natural Language Processing Section, as well as with the Pioneer Centre for AI, at the Department of Computer Science, University of Copenhagen. We are interested in core methodology research on, among others, learning with limited training data and explainable AI; as well as applications thereof to tasks such as fact checking, gender bias detection and question answering. Our group is partly funded by an ERC Starting Grant on Explainable and Robust Automatic Fact Checking, as well as a Sapere Aude Research Leader fellowship on `Learning to Explain Attitudes on Social Media’.

Interests

Natural Language Understanding
Learning with Limited Labelled Data
Explainable AI
Fact Checking
Question Answering
Gender Bias Detection

News

More News

Funded PhD and postdoc positions for start Autumn 2026

25 Nov 2025

2026 PhD Fellowship Call

We are recruiting a PhD and postdoc each for an interdisciplinary project on ‘Human-Centered Explainable Retrieval-Augmented LLMs’ funded by the Independent Research Foundation Denmark, led by Isabelle Augenstein and Irina Shklovski.

Explainability Project

8 Papers Accepted to EMNLP 2025

4 Nov 2025

The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025)

8 papers by CopeNLU authors are accepted to appear at EMNLP 2025, on topics including explainability and cross-cultural NLP.

PhD fellowships for start in Spring or Autumn 2026

1 Sep 2025

2026 PhD Fellowship Call

We are recruiting 1) PhD students interested in explainable NLU for a start in Spring 2026 as part of the ExplainYourself project, 2) a PhD student interested in Trustworthy NLP for a start in Spring 2026, and 3) PhD students on any topic within the focus areas of our lab for a start in Autumn 2026 via ELLIS.

3 Papers to be Presented at ACL 2025

27 Jul 2025

2025 Annual Conference of the Association for Computational Linguistics (ACL 2025)

3 papers by CopeNLU authors are accepted to be presented ACL 2025, on topics including fact checking, retrieval-augmented generation and cultural NLP.

4 Papers Accepted to NAACL 2025

29 Apr 2025

2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL 2025)

4 papers by CopeNLU authors are accepted to appear at NAACL 2025, on topics including interpretability and computational social science.

5 Papers Accepted to EMNLP 2024

12 Nov 2024

The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024)

5 papers by CopeNLU authors are accepted to appear at EMNLP 2024, on topics including factuality and probing for bias.

PhD fellowship on Interpretable Machine Learning available

8 Nov 2024

PhD Fellowship Call

PhD fellowship on Interpretable Machine Learning available. The successfull candidate will be supervised by Pepa Atanasova and Isabelle Augenstein.

Fact Checking Project Explainability Project

Pepa has been appointed as a Tenure-Track Assistant Professor

1 Sep 2024

Pepa appointed as a Tenure-Track Assistant Professor

Starting in September 2024, Pepa is taking on a new role as Tenure-Track Assistant Professor in the NLP Section at the University of Copenhagen.

Fact Checking Project Explainability Project

Participate in research on explainable fact checking

1 Jun 2024

We are recruiting professional fact checkers to take part in an interview and/or a survey about their experiences of fact checking and fact checking technologies.

Fact Checking Project Explainability Project

Outstanding paper award at EACL 2024

17 Mar 2024

The 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024)

Our paper on measuring the fragility of natural language inference models has won an outstanding paper award at EACL 2024!

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People

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Members Affiliated Alumni All

Isabelle Augenstein

Full Professor

Isabelle’s main research interests are natural language understanding, explainability and learning with limited training data.

Pepa Atanasova

Assistant Professor

Pepa’s research interests include the development, diagnostics, and application of explainability and interpretability techniques for NLP models.

Qianli Wang

PhD Intern

Qianli is a PhD student at TU Berlin & DFKI and was visiting CopeNLU in Summer/Autumn 2025. His research interests are explainability, faithfulness, and multilinguality.

Na Min An

PhD Intern

Na Min An’s research interests are explainability, multimodal systems, and human-centered AI. She visited CopeNLU in Spring/Summer 2025.

Erik Arakelyan

PhD Student

Erik’s main research interests are question answering and explainability. He successfully defended his PhD thesis in April 2025, and is now a Machine Learning Researcher at NVIDIA.

Nadav Borenstein

Postdoc

Nadav’s research interests include improving the trustworthiness and usefulness of deep models in the NLP domain.

Arnav Arora

PhD Student

Arnav’s research interests include equitable ML, mitigating online harms, and the intersection of NLP and Computational Social Science.

Haeun Yu

PhD Student

Haeun’s main research interests include enhancing explainability in fact-checking and transparency of knowledge-enhanced LM.

Jingyi Sun

PhD Student

Jingyi Sun’s research interests include explainability, fact-checking, and question answering.

Lovisa Hagström

PhD Intern

Lovisa was a visiting PhD student from Chalmers University of Technology. Her research interests include language model controllability, interpretability and retrieval-augmented methods.

Siddhesh Pawar

PhD Student

Siddhesh Pawar’s research interests include multilingual models, fairness and accountability in NLP systems.

Dustin Wright

Postdoc

Dustin is a DDSA postdoctoral fellow, working on scientific natural language understanding and faithful text generation.

Ercong Nie

PhD Intern

Ercong is a PhD student at LMU Munich whose research interests include multilingual NLP, efficient methods for NLP, human-inspired NLP, and interpretability.

Steffen Eger

Visiting Professor

Steffen Eger is full professor at the University of Technology Nuremberg, Germany, working mainly on text generation and its evaluation. He was visiting CopeNLU in Winter/Spring 2024.

Ran Zhang

PhD Intern

Ran Zhang is a PhD student at Mannheim University, working on text generation and its evaluation. She was visiting CopeNLU in Winter/Spring 2024.

Christoph Leitner

PhD Intern

Christoph Leitner is a PhD student at Mannheim University, working on explainable text generation metrics. He was visiting CopeNLU in Winter/Spring 2024.

Greta Warren

Postdoc

Greta’s research interests include user-centred explainability, fact-checking, and human-AI interaction.

Shuzhou Yuan

PhD Intern

Shuzhou Yuan is a PhD student at Dresden University of Technology, working on the application of graph neural networks for natural language processing. He was visiting CopeNLU in Spring 2024.

Sekh Mainul Islam

PhD Student

Sekh’s research interests include explainability in fact checking and improving robustness and trustworthiness in NLP models.

Sarah Masud

Postdoc

Sarah broadly works in the area of computational social systems with a focus on news narrative and hate speech modelling. Her PhD at IIIT-Delhi was supported by fellowships from Google and PMRF.

Zain Muhammad Mujahid

PhD Student

Zain’s main research interests include disinformation detection, fact-checking, and factual text generation.

Yoonna Jang

Postdoc

Yoonna’s research interests include language generation, factuality and interpretability.

Yijun Bian

Postdoc

Yijun is a Marie-Curie postdoctoral fellow working on fair and interpretable ML.

Ryan Cotterell

Lecturer

This is Ryan. He’s a lecturer at the University of Cambridge and a frequent collaborator of the CopeNLU group.

Sara Vera Marjanovic

PhD Student

Sara’s research interests include explainable IR and NLP models, identifying biases in large text datasets, as well as working with social media data. She is a member of the DIKU ML section and IR group and co-advised by Isabelle.

Amalie Brogaard Pauli

PhD Student

Amalie’s research focuses on detecting persuasive and misleading text. She is a PhD student at Aarhus University and co-advised by Isabelle.

Lucas Resck

PhD Student

Lucas is an ELLIS PhD student at the University of Cambridge, supervised by Anna Corhonen and co-supervised by Isabelle. His research interests include machine learning, NLP and explainability.

Ahmad Dawar Hakimi

PhD Student

Dawar is an ELLIS PhD student at LMU Munich, supervised by Hinrich Schütze and co-supervised by Isabelle. His research interests include mechanistic interpretability, summarisation and factuality of LLMs.

Karolina Stanczak

PhD Student

Karolina’s research interests include gender-biased language detection and statistical methods. She successfully defended her PhD thesis in January 2024, and is now a postdoctoral researcher at ETH, Zurich.

Marta Marchiori Manerba

PhD Intern

Marta’s research interests are explainability and fairness, with a focus on abusive language detection. She is a PhD student at the University of Pisa and was visiting CopeNLU in Spring/Summer 2023.

Kevin Roitero

Visiting Researcher

Kevin Roitero is an Assistant Professor (RTD/a) at the University of Udine, Italy. His research interests include Artificial Intelligence, Deep Learning, Crowdsourcing, and Information Retrieval. He was visiting CopeNLU in Spring/Summer 2023.

Nils Rethmeier

PhD Student

Nils is the Head of Artificial Intelligence at experial.ai. He was previously a PhD student in CopeNLU, researching low-resource learning and unsupervised learning as well as explainability. He successfully defended his PhD thesis in August 2023.

Amelie Wührl

PhD Intern

Amelie is a Postdoctoral Researcher at the IT University of Copenhagen, interested in fact-checking, specifically for medical claims and content that people chose to share on social media. She was visiting CopeNLU in Spring 2023 to work on scientific fact checking.

Lucie-Aimée Kaffee

Postdoc

Lucie’s research interests include supporting lower-resourced language communities (including Wikipedia and Wikidata) with NLP, and multilingual knowledge graphs. She was a postdoctoral researcher in the CopeNLU group from 2021-2023 and is now an Applied Policy Researcher at Hugging Face.

Oscar Kjell

Postdoc

Oscar researches automating the assessment of psychological constructs using representation learning. He was previously an international postdoc affiliated with Lund University as well the University of Copenhagen and Stony Brook University.

Andreas Nugaard Holm

PhD Student

Andreas is an AI Specialist at Alexandra Institute. His was a PhD student in the group from 2019-2023, researching representation learning and domain adaptation with a focus on scientific texts.

Thea Rolskov Sloth

Intern

Thea is a Data Scientist at Aarhus Municipality. She was MSc Student in Cognitive Science at Aarhus University, visiting CopeNLU in Autumn 2022 to work on gender bias detection.

Miryam de Lhoneux

Postdoc

Miryam’s main research interests are syntactic parsing, multilingual NLP and interpretability. She is an assistant profesor at KU Leuven, and was previously an international postdoc affiliated with KU Leuven as well as the University of Copenhagen.

Nodens Koren

PhD Student

Nodens is interested in interpretability and bio-inspired models. He is now a PhD student at ETH Zurich.

Klim Zaporojets

PhD Intern

Klim is a Postdoctoral Researcher at Aarhus University. He was visiting CopeNLU in Spring 2022 to work on entity linking.

Sagnik Ray Choudhury

Postdoc

Sagnik is an Assistant Professor at the University of North Texas. He was a postdoctoral researcher in CopeNLU from 2020 to 2022, working on question answering and interpretability of neural black-box models.

Shailza Jolly

PhD Intern

Shailza is a Senior Machine Learning Scientist at Flinn.ai. She was visiting CopeNLU in Winter/Spring 2021 to work on interpretability.

Yova Kementchedjhieva

PhD Student

Yova was a PhD student at the University of Copenhagen co-advised by Isabelle, researching low-resource and cross-lingual learning. She is now an Assistant Professor at Mohamed bin Zayed University of Artificial Intelligence, UAE.

Ana Valeria González

PhD Student

Ana is a senior data scientist at Halfspace, Copenhagen. Prior to this, she was a PhD student in the CoAStaL NLP group from 2017-2021 co-advised by Isabelle, working on question answering.

Andrea Lekkas

PhD Student

Andrea’s main research interests are multilingual learning and language modelling. He was a PhD student at the University of Southern Denmark and co-advised by Isabelle, and is now a Data Scientist at Allianz Technology.

Johannes Bjerva

Postdoc

Johannes is a full professor at Aalborg University Copenhagen. He was a postdoc in CopeNLU from 2017 to 2020, researching multi-lingual and multi-task learning.

Liesbeth Allein

PhD Intern

Liesbeth is a Postdoctoral Researcher at KU Leuven and Ghent University, and was visiting CopeNLU in Spring 2020 to work on fact checking.

Wei Zhao

PhD Intern

Wei Zhao is a lecturer at the University of Aberdeen, and was visiting CopeNLU in Winter 2019 to work on low-resource natural language generation.

Mareike Hartmann

PhD Student

Mareike was a PhD student at the University of Copenhagen co-advised by Isabelle, researching disinformation detection, and is now a postdoctoral researcher at Saarland University.

Farhad Nooralahzadeh

PhD Intern

Farhad is currently a senior postdoctoral researcher at the University of Zurich. He was visiting CopeNLU in Spring 2019 to work on domain adaptation for information extraction.

Zhong Xuan

Research Intern

Zhong Xuan is a senior data scientist at Grab, Singapore, and was visiting CopeNLU in Summer 2019 to work on relation extraction and knowledge base population.

Luna De Bruyne

PhD Intern

Luna is a Tenure-Track Assistant Professor at the University of Antwerp. She was visiting CopeNLU in Spring 2019 to work on emotion detection.

Giannis Bekoulis

PhD Intern

Giannis is a principal data scientist at Clarivate, and was visiting CopeNLU as a PhD Intern in Spring 2019 to work on joint information extraction.

Sune Debel

Research Assistant

Now a senior data scientist at ZeroNorth.

Featured Publications

Factuality Challenges in the Era of Large Language Models

The emergence of tools based on large language models (LLMs), like OpenAI’s ChatGPT and Google’s Gemini, has garnered immense public attention due to their advanced natural language generation capabilities. These remarkably natural-sounding tools have the potential to be highly useful across various tasks. However, they also tend to produce false, erroneous, or misleading content – commonly referred to as hallucinations. Additionally, LLMs can be misused to generate convincing yet false content and profiles on a large scale, posing a substantial societal challenge by potentially deceiving users and spreading inaccurate information. This makes fact-checking increasingly important. Despite their issues with factual accuracy, LLMs have shown proficiency in various subtasks that support fact-checking, which is essential for ensuring factually accurate responses. In light of these concerns, we explore the issues related to factuality in LLMs and their impact on fact-checking. We identify key challenges, imminent threats, and possible solutions to these factuality issues. We also thoroughly examine these challenges, existing solutions, and potential prospects for fact-checking. By analysing the factuality constraints within LLMs and their impact on fact-checking, we aim to contribute to a path towards maintaining accuracy at a time of confluence of generative AI and misinformation.

Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty, Giovanni Luca Ciampaglia, David Corney, Renee DiResta, Emilio Ferrara, Scott Hale, Alon Halevy, Eduard Hovy, Heng Ji, Filippo Menczer, Ruben Miguez, Preslav Nakov, Dietram Scheufele, Shivam Sharma, Giovanni Zagni

In Nature Machine Intelligence, 2024

PDF Fact Checking Project Explainability Project

Recent Publications

More Publications

Stress Testing Factual Consistency Metrics for Long-Document Summarization

Evaluating the factual consistency of abstractive text summarization remains a significant challenge, particularly for long documents, …

Zain Muhammad Mujahid, Dustin Wright, Isabelle Augenstein

PDF Fact Checking Project

Evaluation Framework for Highlight Explanations of Context Utilisation in Language Models

Context utilisation, the ability of Language Models (LMs) to incorporate relevant information from the provided context when generating …

Jingyi Sun, Pepa Atanasova, Sagnik Ray Choudhury, Sekh Mainul Islam, Isabelle Augenstein

PDF Fact Checking Project

Expanding Computation Spaces of LLMs at Inference Time

Chain-of-thought (CoT) rationale enables language models to use additional task-related text for problem-solving, benefiting not only …

Yoonna Jang, Kisu Yang, Isabelle Augenstein

PDF Limited Data Project

Multi-Step Knowledge Interaction Analysis via Rank-2 Subspace Disentanglement

Natural Language Explanations (NLEs) describe how Large Language Models (LLMs) make decisions, drawing on both external Context …

Sekh Mainul Islam, Pepa Atanasova, Isabelle Augenstein

PDF Explainability Project

A Meta-Evaluation of Style and Attribute Transfer Metrics

LLMs make it easy to rewrite text in any style, be it more polite, persuasive, or more positive. We present a large-scale study of …

Amalie Brogaard Pauli, Isabelle Augenstein, Ira Assent

PDF Limited Data Project

Explainability and Interpretability of Multilingual Large Language Models: A Survey

Multilingual large language models (MLLMs) demonstrate state-of-the-art capabilities across diverse cross-lingual and multilingual …

Lucas Resck, Isabelle Augenstein, Anna Korhonen

PDF Limited Data Project Explainability Project

FLARE: Faithful Logic-Aided Reasoning and Exploration

Modern Question Answering (QA) and Reasoning approaches based on Large Language Models (LLMs) commonly use prompting techniques, such …

Erik Arakelyan, Pasquale Minervini, Pat Verga, Patrick Lewis, Isabelle Augenstein

PDF Explainability Project Knowledge Bases Project

Graph-Guided Textual Explanation Generation Framework

Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model’s reasoning about …

Shuzhou Yuan, Jingyi Sun, Ran Zhang, Michael Färber, Steffen Eger, Pepa Atanasova, Isabelle Augenstein

PDF Explainability Project Fact Checking Project

Multi-Modal Framing Analysis of News

Automated frame analysis of political communication is a popular task in computational social science that is used to study how authors …

Arnav Arora, Srishti Yadav, Maria Antoniak, Serge Belongie, Isabelle Augenstein

PDF Limited Data Project Explainability Project Fact Checking Project

Presumed Cultural Identity: How Names Shape LLM Responses

Names are deeply tied to human identity. They can serve as markers of individuality, cultural heritage, and personal history. However, …

Siddhesh Pawar, Arnav Arora, Lucie-Aimée Kaffee, Isabelle Augenstein

PDF Multilingual Project Bias Detection Project

Projects

All Language Understanding Weak Supervision

Contact

augenstein@di.ku.dk
Øster Voldgade 3, Copenhagen, Denmark

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