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. The successfull candidate will be supervised by Pepa Atanasova and Isabelle Augenstein.

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.

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.

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

A PhD and two postdoc positions on natural language understanding are available. The positions are funded by the Pioneer Centre for AI.

5 papers by CopeNLU authors are accepted to appear at EMNLP 2023, ranging from explainability to language modelling.

A PhD position on explainable natural language understanding is available in CopeNLU. The positions is funded by the ERC Starting Grant project ExplainYourself, and applications are due by 1 February 2024.

On 1 September 2023, the ERC Starting Grant project ExplainYourself on ‘Explainable and Robust Automatic Fact Checking’ is kicking off, and two new PhD students are joining our group.

4 papers by CopeNLU authors are accepted to appear at ACL 2023, on faithfulness of explanations, measuring intersectional biases, event extraction and few-shot stance detection.

People

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Full Professor

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

Assistant Professor

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

PhD Student

Erik’s main research interests are question answering and explainability.

PhD Student

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

PhD Student

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

PhD Student

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

PhD Student

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

PhD Intern

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

PhD Student

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

Postdoc

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

PhD Intern

Ercong’s research interests include multilingual NLP, efficient methods for NLP, human-inspired NLP, and interpretability.

Visiting Professor

Steffen Eger is Heisenberg and BMBF group leader at University of Mannheim, Germany, working mainly on text generation and its evaluation. He was visiting CopeNLU in Winter/Spring 2024.

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.

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.

Postdoc

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

PhD Intern

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

PhD Student

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

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.

PhD Student

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

Lecturer

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

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.

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.

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.

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.

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 Mila, Montreal.

PhD Intern

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

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.

PhD Student

Nils researches low-resource learning and unsupervised learning as well as explainability. He successfully defended his PhD thesis in August 2023.

PhD Intern

Amelie is a PhD Student at the University of Stuttgart, 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.

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 a postdoctoral researcher at Hasso-Plattner-Institut.

Postdoc

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

PhD Student

Andreas’ main research areas are representation learning and domain adaptation, with a focus on scientific texts.

Intern

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

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.

PhD Student

Nodens is interested in interpretability and bio-inspired models.

PhD Intern

Klim is a PhD Student at Ghent University. He was visiting CopeNLU in Spring 2022 to work on entity linking.

Postdoc

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

PhD Intern

Shailza is a PhD Student at the Technical University of Kaiserslautern, and a research assistant at DFKI. She was visiting CopeNLU in Winter/Spring 2021 to work on interpretability.

PhD Student

Yova was a PhD student in the CoAStaL NLP group researching low-resource and cross-lingual learning, co-advised by Isabelle. She is now a postdoc in the same group.

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.

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.

Postdoc

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

PhD Intern

Liesbeth is a PhD Student at KU Leuven, and was visiting CopeNLU in Spring 2020 to work on fact checking.

PhD Intern

Wei Zhao is a postdoctoral researcher at Heidelberg University, and was visiting CopeNLU in Winter 2019 to work on low-resource natural language generation.

PhD Student

Mareike was a member of the CoAStaL NLP group, researching disinformation detection and co-advised by Isabelle, and is now a researcher at DFKI.

PhD Intern

Farhad is a currently postdoc at the University of Zurich, and was visiting CopeNLU in Spring 2019 to work on domain adaptation for information extraction.

Research Intern

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

PhD Intern

Luna is a PhD Student at Ghent University, and was visiting CopeNLU in Spring 2019 to work on emotion detection.

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.

Research Assistant

Now a machine learning engineer at Omhu.

Recent Publications

More Publications

Question Answering (QA) datasets have been instrumental in developing and evaluating Large Language Model (LLM) capabilities. However, …

Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs …

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

Much theoretical work has described the ability of transformer language models (LMs) to represent formal languages.However, linking …

Knowledge-intensive language understanding tasks require Language Models (LMs) to integrate relevant context, mitigating their inherent …

The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the …

Uncovering latent values and opinions in large language models (LLMs) can help identify biases and mitigate potential harm. Recently, …

Large language models have been shown to encode a variety of social biases, which carries the risk of downstream harms. While the …

The large and ever-increasing amount of data available on the Internet coupled with the laborious task of manual claim and fact …

The emergence of tools based on large language models (LLMs), like OpenAI’s ChatGPT and Google’s Gemini, has garnered immense public …

Recent Posts

We gave a tutorial including lab session at the first ALPS (Advanced Language Prcoessing School. If you want to learn more about …

Yes, we all have planned to be under palm trees of Punta Cana now and to sip drinks with umbrellas. Let’s make a new plan: an …

The University of Copenhagen is a great place if you’re both interested in high-quality NLP research and a high quality of life.

Projects

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Learning with Limited Labelled Data

Learning with limited labelled data, including multi-task learning, weakly supervised and zero-shot learning

Stance Detection and Fact Checking

Determine the attitude expressed in a text towards a topic, and use this for automatic evidence-based fact checking

Explainable Machine Learning

Explaining relationships between inputs and outputs of black-box machine learning models

Gender Bias Detection

Automatically detecting gendered language, and to what degree attitudes towards entities are influenced by gender bias

Multilingual Learning and Multicultural Learning

Training models to work well for multiple languages and cultures, including low-resource ones

Scholarly Data Processing

Automatically processing scholarly data to assist researchers in finding publications, writing better papers, or tracking their impact.

Question Answering

Answering questions automatically, including in conversational settings

Knowledge Base Population

Extract information about entities, phrases and relations between them from text to populate knowledge bases

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