Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Stuermer, Matthias"'
Predicting case criticality helps legal professionals in the court system manage large volumes of case law. This paper introduces the Criticality Prediction dataset, a new resource for evaluating the potential influence of Swiss Federal Supreme Court
Externí odkaz:
http://arxiv.org/abs/2410.13460
Autor:
Rolshoven, Luca, Rasiah, Vishvaksenan, Bose, Srinanda Brügger, Stürmer, Matthias, Niklaus, Joel
Legal research is a time-consuming task that most lawyers face on a daily basis. A large part of legal research entails looking up relevant caselaw and bringing it in relation to the case at hand. Lawyers heavily rely on summaries (also called headno
Externí odkaz:
http://arxiv.org/abs/2410.13456
Autor:
Stuermer, Matthias
This report for the attention of the Federal Department of Foreign Affairs (FDFA) makes a scientific contribution in the context of postulate 22.4411 "Digital Sovereignty Strategy for Switzerland" by Councillor of States Heidi Z'graggen. The report s
Externí odkaz:
http://arxiv.org/abs/2406.03266
The assessment of explainability in Legal Judgement Prediction (LJP) systems is of paramount importance in building trustworthy and transparent systems, particularly considering the reliance of these systems on factors that may lack legal relevance o
Externí odkaz:
http://arxiv.org/abs/2402.17013
Releasing court decisions to the public relies on proper anonymization to protect all involved parties, where necessary. The Swiss Federal Supreme Court relies on an existing system that combines different traditional computational methods with human
Externí odkaz:
http://arxiv.org/abs/2310.04632
Resolving the scope of a negation within a sentence is a challenging NLP task. The complexity of legal texts and the lack of annotated in-domain negation corpora pose challenges for state-of-the-art (SotA) models when performing negation scope resolu
Externí odkaz:
http://arxiv.org/abs/2309.08695
Anonymity of both natural and legal persons in court rulings is a critical aspect of privacy protection in the European Union and Switzerland. With the advent of LLMs, concerns about large-scale re-identification of anonymized persons are growing. In
Externí odkaz:
http://arxiv.org/abs/2308.11103
Autor:
Stern, Ronja, Rasiah, Vishvaksenan, Matoshi, Veton, Bose, Srinanda Brügger, Stürmer, Matthias, Chalkidis, Ilias, Ho, Daniel E., Niklaus, Joel
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 LLM capabilities. However, domain-specific and multilingual benchmarks
Externí odkaz:
http://arxiv.org/abs/2306.09237
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 and the available ones are often only for the English language. We cura
Externí odkaz:
http://arxiv.org/abs/2306.02069
Sentence Boundary Detection (SBD) is one of the foundational building blocks of Natural Language Processing (NLP), with incorrectly split sentences heavily influencing the output quality of downstream tasks. It is a challenging task for algorithms, e
Externí odkaz:
http://arxiv.org/abs/2305.01211