48 citations · 85 across the 19 of their papers we have counts for
6 papers · 1 filter
On the Interplay between Fairness and Explainability
Stephanie Brandl, Emanuele Bugliarello, Ilias Chalkidis
In order to build reliable and trustworthy NLP applications, models need to be both fair across different demographics and explainable. Usually these two objectives, fairness and e…
Rather a Nurse than a Physician -- Contrastive Explanations under Investigation
Oliver Eberle, Ilias Chalkidis, Laura Cabello +1
Contrastive explanations, where one decision is explained in contrast to another, are supposed to be closer to how humans explain a decision than non-contrastive explanations, wher…
One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support
Ronja Stern, Vishvaksenan Rasiah, Veton Matoshi +5
Recent strides in Large Language Models (LLMs) have saturated many Natural Language Processing (NLP) benchmarks, emphasizing the need for more challenging ones to properly assess L…
MultiLegalPile: A 689GB Multilingual Legal Corpus
Joel Niklaus, Veton Matoshi, Matthias Stürmer +2
Large, high-quality datasets are crucial for training Large Language Models (LLMs). However, so far, there are few datasets available for specialized critical domains such as law a…
LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development
Ilias Chalkidis, Nicolas Garneau, Catalina Goanta +2
In this work, we conduct a detailed analysis on the performance of legal-oriented pre-trained language models (PLMs). We examine the interplay between their original objective, acq…
LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain
Joel Niklaus, Veton Matoshi, Pooja Rani +3
Lately, propelled by the phenomenal advances around the transformer architecture, the legal NLP field has enjoyed spectacular growth. To measure progress, well curated and challeng…