Papers by Antonia Karamolegkou
Copyright Violations and Large Language Models (2023.emnlp-main)
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| Challenge: | a recent study examines the extent to which language models can memorize training data . a fair use exemption to copyright laws allows for limited use of copyrighted material . |
| Approach: | They examine the extent to which language models can redistribute copyrighted text . they use a range of popular books and coding problems to study copyright violations . |
| Outcome: | This study examines the extent to which language models can redistribute copyrighted text . it shows that language models may memorize entire chunks of training data . |
Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statements (2025.naacl-long)
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Antonia Karamolegkou, Sandrine Schiller Hansen, Ariadni Christopoulou, Filippos Stamatiou, Anne Lauscher, Anders Søgaard
| Challenge: | a survey shows that laypeople express different ethical concerns than professionals . acl-code-ethics provides a taxonomy for ethical concerns . |
| Approach: | They propose to annotate a corpus of ethical concern statements from scientific papers . they extract ethical concern keywords from the statements and automate the process . |
| Outcome: | The proposed corpus of ethical concern statements compares with existing taxonomies and guidelines pointing to gaps and actionable insights. |
Mapping Brains with Language Models: A Survey (2023.findings-acl)
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| Challenge: | accumulated evidence for brain and language model activations remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism. |
| Approach: | They examine the evidence accumulated by 30 studies spanning 10 datasets and 8 metrics to determine whether there is any overlap between brain and language model activations. |
| Outcome: | The findings suggest that representations extracted from NLP models can (partially) explain the signal found in neural data. |
Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detection (2026.eacl-long)
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Ivan Vykopal, Antonia Karamolegkou, Jaroslav Kopčan, Qiwei Peng, Tomáš Javůrek, Michal Gregor, Marian Simko
| Challenge: | Recent advances in multilingual Large Language Models have enabled powerful capabilities for cross-lingual fact-checking. |
| Approach: | They evaluate six open-source multilingual LLMs across 20 languages using a fully multilingual prompting strategy. |
| Outcome: | The proposed model performs better on high-resource languages than on low-resourced ones. |
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features (2023.findings-emnlp)
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| Challenge: | Current knowledge is limited on whether cultural features can predict cross-cultural transfer learning success for subjective tasks. |
| Approach: | They advocate integration of cultural information into datasets and cultural adaptability . findings suggest cultural features can predict cross-cultural transfer learning success . |
| Outcome: | The findings suggest that cultural features can predict cross-cultural transfer learning success in OLD tasks. |
Trick or Neat: Adversarial Ambiguity and Language Model Evaluation (2025.findings-acl)
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| Challenge: | Direct prompting fails to detect ambiguity while linear probes can decode ambiguities with high accuracy, sometimes exceeding 90%. |
| Approach: | They introduce an adversarial ambiguity dataset that includes syntactic, lexical, and phonological ambiguities along with adversarials. |
| Outcome: | The proposed dataset includes syntactic, lexical, and phonological ambiguities along with adversarial variations. |
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment (2026.eacl-long)
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Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazar, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg Schulten, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva
| Challenge: | This paper surveys work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Approach: | This paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Outcome: | The paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users (2025.acl-long)
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Antonia Karamolegkou, Malvina Nikandrou, Georgios Pantazopoulos, Danae Sanchez Villegas, Phillip Rust, Ruchira Dhar, Daniel Hershcovich, Anders Søgaard
| Challenge: | Despite high adoption rate of Large Language Models, there are limitations related to contextual understanding, cultural sensitivity, and complex scene understanding. |
| Approach: | They conduct a user survey to identify adoption patterns and key challenges users face with such technologies. |
| Outcome: | The proposed models have high adoption rates but still face limitations in visual aids. |