most citedUncertainty in Natural Language Generation: From Theory to Applications

7 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Non-Exchangeable Conformal Language Generation with Nearest Neighbors

Dennis Ulmer, Chrysoula Zerva, André F. T. Martins

Quantifying uncertainty in automatically generated text is important for letting humans check potential hallucinations and making systems more reliable. Conformal prediction is an…

cs.CL20237 cited

Uncertainty in Natural Language Generation: From Theory to Applications

Joris Baan, Nico Daheim, Evgenia Ilia +7

Recent advances of powerful Language Models have allowed Natural Language Generation (NLG) to emerge as an important technology that can not only perform traditional tasks like sum…

cs.CL2023

Conformalizing Machine Translation Evaluation

Chrysoula Zerva, André F. T. Martins

Several uncertainty estimation methods have been recently proposed for machine translation evaluation. While these methods can provide a useful indication of when not to trust mode…

cs.CL20232 cited

BLEU Meets COMET: Combining Lexical and Neural Metrics Towards Robust Machine Translation Evaluation

Taisiya Glushkova, Chrysoula Zerva, André F. T. Martins

Although neural-based machine translation evaluation metrics, such as COMET or BLEURT, have achieved strong correlations with human judgements, they are sometimes unreliable in det…

cs.CL2023

Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors

Giorgos Filandrianos, Edmund Dervakos, Orfeas Menis-Mastromichalakis +2

In the wake of responsible AI, interpretability methods, which attempt to provide an explanation for the predictions of neural models have seen rapid progress. In this work, we are…