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20242026
most citedNative Design Bias: Studying the Impact of English Nativeness on Language Model Performance

3 citations · 3 across the 5 of their papers we have counts for

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cs.CL20253 cited

Native Design Bias: Studying the Impact of English Nativeness on Language Model Performance

Manon Reusens, Philipp Borchert, Jochen De Weerdt +1

Large Language Models (LLMs) excel at providing information acquired during pretraining on large-scale corpora and following instructions through user prompts. This study investiga…

cs.CL2025

Domain Adaptation of LLMs for Process Data

Rafael Seidi Oyamada, Jari Peeperkorn, Jochen De Weerdt +1

In recent years, Large Language Models (LLMs) have emerged as a prominent area of interest across various research domains, including Process Mining (PM). Current applications in P…

cs.CL2025

Bridging Language Gaps: Enhancing Few-Shot Language Adaptation

Philipp Borchert, Jochen De Weerdt, Marie-Francine Moens

The disparity in language resources poses a challenge in multilingual NLP, with high-resource languages benefiting from extensive data, while low-resource languages lack sufficient…

cs.CL2025

Language Fusion for Parameter-Efficient Cross-lingual Transfer

Philipp Borchert, Ivan Vulić, Marie-Francine Moens +1

Limited availability of multilingual text corpora for training language models often leads to poor performance on downstream tasks due to undertrained representation spaces for lan…

cs.CL2024

Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning

Philipp Borchert, Jochen De Weerdt, Marie-Francine Moens

Differentiating relationships between entity pairs with limited labeled instances poses a significant challenge in few-shot relation classification. Representations of textual data…