4 papers
Speaking in Self-Assessing Tongues: On the Verbalized Confidence of LLMs in Machine Translation
Ali Marashian, Alexis Palmer, Katharina von der Wense
The rapid rise in popularity of large language models (LLMs) for translation calls for a thorough study of the reliability of their confidence in their own outputs. Unlike many gen…
Large Language Models Are Overconfident in Their Own Responses
Mario Sanz-Guerrero, Manuel Mager, Katharina von der Wense
Prior work has shown that instruction-tuned large language models (LLMs) are less well calibrated than their base pre-trained counterparts. However, little is known about the frequ…
Model-Based Ranking of Source Languages for Zero-Shot Cross-Lingual Transfer
Abteen Ebrahimi, Adam Wiemerslage, Katharina von der Wense
We present NN-Rank, an algorithm for ranking source languages for cross-lingual transfer, which leverages hidden representations from multilingual models and unlabeled target-langu…
Improving Low-Resource Morphological Inflection via Self-Supervised Objectives
Adam Wiemerslage, Katharina von der Wense
Self-supervised objectives have driven major advances in NLP by leveraging large-scale unlabeled data, but such resources are scarce for many of the world's languages. Surprisingly…