Antonia Karamolegkou — accepted AI-conference papers

2025

Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statements

Antonia Karamolegkou, Sandrine Schiller Hansen, Ariadni Christopoulou, Filippos Stamatiou, Anne Lauscher, Anders Søgaard

NAACL 2025long

What ethical concerns, if any, do LLM researchers have? We introduce EthiCon, a corpus of 1,580 ethical concern statements extracted from scientific papers published in the ACL Anthology. We extract ethical concern keywords from the statements and show promising results in automating the concern ide…

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2025

Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users

Antonia Karamolegkou, Malvina Nikandrou, Georgios Pantazopoulos, Danae Sanchez Villegas, Phillip Rust, Ruchira Dhar +2

ACL 2025long

This paper explores the effectiveness of Multimodal Large Language models (MLLMs) as assistive technologies for visually impaired individuals. We conduct a user survey to identify adoption patterns and key challenges users face with such technologies. Despite a high adoption rate of these models, ou…

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2025

Trick or Neat: Adversarial Ambiguity and Language Model Evaluation

Antonia Karamolegkou, Oliver Eberle, Phillip Rust, Carina Kauf, Anders Søgaard

ACL 2025finding

Detecting ambiguity is important for language understanding, including uncertainty estimation, humour detection, and processing garden path sentences. We assess language models’ sensitivity to ambiguity by introducing an adversarial ambiguity dataset that includes syntactic, lexical, and phonologica…

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2023

Copyright Violations and Large Language Models

Antonia Karamolegkou, Jiaang Li, Li Zhou, Anders Søgaard

EMNLP 2023short main

Language models may memorize more than just facts, including entire chunks of texts seen during training. Fair use exemptions to copyright laws typically allow for limited use of copyrighted material without permission from the copyright holder, but typically for extraction of information from copyr…

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2023

Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features

Li Zhou, Antonia Karamolegkou, Wenyu Chen, Daniel Hershcovich

EMNLP 2023long findings

The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD). Current understanding underscores that these…

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2023

Mapping Brains with Language Models: A Survey

Antonia Karamolegkou, Mostafa Abdou, Anders Søgaard

ACL 2023findings

Over the years, many researchers have seemingly made the same observation: Brain and language model activations exhibit some structural similarities, enabling linear partial mappings between features extracted from neural recordings and computational language models. In an attempt to evaluate how mu…

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