6 citations · 9 across the 5 of their papers we have counts for
7 papers · 1 filter
How Important is `Perfect' English for Machine Translation Prompts?
Patrícia Schmidtová, Niyati Bafna, Seth Aycock +4
Large language models (LLMs) have achieved top results in recent machine translation evaluations, but they are also known to be sensitive to errors and perturbations in their promp…
Beyond Literal Token Overlap: Token Alignability for Multilinguality
Katharina Hämmerl, Tomasz Limisiewicz, Jindřich Libovický +1
Previous work has considered token overlap, or even similarity of token distributions, as predictors for multilinguality and cross-lingual knowledge transfer in language models. Ho…
Understanding Cross-Lingual Alignment -- A Survey
Katharina Hämmerl, Jindřich Libovický, Alexander Fraser
Cross-lingual alignment, the meaningful similarity of representations across languages in multilingual language models, has been an active field of research in recent years. We sur…
Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You
Felix Friedrich, Katharina Hämmerl, Patrick Schramowski +4
Text-to-image generation models have recently achieved astonishing results in image quality, flexibility, and text alignment, and are consequently employed in a fast-growing number…
Exploring Anisotropy and Outliers in Multilingual Language Models for Cross-Lingual Semantic Sentence Similarity
Katharina Hämmerl, Alina Fastowski, Jindřich Libovický +1
Previous work has shown that the representations output by contextual language models are more anisotropic than static type embeddings, and typically display outlier dimensions. Th…
Do Multilingual Language Models Capture Differing Moral Norms?
Katharina Hämmerl, Björn Deiseroth, Patrick Schramowski +3
Massively multilingual sentence representations are trained on large corpora of uncurated data, with a very imbalanced proportion of languages included in the training. This may ca…
Combining Static and Contextualised Multilingual Embeddings
Katharina Hämmerl, Jindřich Libovický, Alexander Fraser
Static and contextual multilingual embeddings have complementary strengths. Static embeddings, while less expressive than contextual language models, can be more straightforwardly…