3 papers
cs.CL2026
Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution
Janiça Hackenbuchner, Jasper Degraeuwe, Arda Tezcan +1
Machine translation (MT) systems continue to produce gender-biased translations. In a time where self-expression is paramount, mistranslations based on default behaviour and stereo…
cs.CL2026
Beyond Reproduction: A Paired-Task Framework for Assessing LLM Comprehension and Creativity in Literary Translation
Ran Zhang, Steffen Eger, Arda Tezcan +3
Large language models (LLMs) are increasingly used for creative tasks such as literary translation. Yet translational creativity remains underexplored and is rarely evaluated at sc…
cs.CL2026
What Triggers my Model? Contrastive Explanations Inform Gender Choices by Translation Models
Janiça Hackenbuchner, Arda Tezcan, Joke Daems
Interpretability can be implemented to understand decisions taken by (black box) models, such as neural machine translation (NMT) or large language models (LLMs). Yet, research in…