Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Benyekhlef, Karim"'
Autor:
Tan, Jinzhe, Westermann, Hannes, Pottanigari, Nikhil Reddy, Šavelka, Jaromír, Meeùs, Sébastien, Godet, Mia, Benyekhlef, Karim
Mediation is a dispute resolution method featuring a neutral third-party (mediator) who intervenes to help the individuals resolve their dispute. In this paper, we investigate to which extent large language models (LLMs) are able to act as mediators.
Externí odkaz:
http://arxiv.org/abs/2410.07053
Encoding legislative text in a formal representation is an important prerequisite to different tasks in the field of AI & Law. For example, rule-based expert systems focused on legislation can support laypeople in understanding how legislation applie
Externí odkaz:
http://arxiv.org/abs/2311.04911
Autor:
Westermann, Hannes, Benyekhlef, Karim
Laypeople (i.e. individuals without legal training) may often have trouble resolving their legal problems. In this work, we present the JusticeBot methodology. This methodology can be used to build legal decision support tools, that support laypeople
Externí odkaz:
http://arxiv.org/abs/2308.02032
Publikováno v:
Proceedings of the ICAIL 2023 Workshop on Artificial Intelligence for Access to Justice co-located with 19th International Conference on AI and Law (ICAIL 2023)
In this article, we introduce LLMediator, an experimental platform designed to enhance online dispute resolution (ODR) by utilizing capabilities of state-of-the-art large language models (LLMs) such as GPT-4. In the context of high-volume, low-intens
Externí odkaz:
http://arxiv.org/abs/2307.16732
We propose an adaptive environment (CABINET) to support caselaw analysis (identifying key argument elements) based on a novel cognitive computing framework that carefully matches various machine learning (ML) capabilities to the proficiency of a user
Externí odkaz:
http://arxiv.org/abs/2210.13635
Machine learning research typically starts with a fixed data set created early in the process. The focus of the experiments is finding a model and training procedure that result in the best possible performance in terms of some selected evaluation me
Externí odkaz:
http://arxiv.org/abs/2201.06653
Publikováno v:
Frontiers in Artificial Intelligence and Applications, Volume 334: Legal Knowledge and Information Systems, 2020, pp. 164-173
Human-performed annotation of sentences in legal documents is an important prerequisite to many machine learning based systems supporting legal tasks. Typically, the annotation is done sequentially, sentence by sentence, which is often time consuming
Externí odkaz:
http://arxiv.org/abs/2112.11494
Autor:
Savelka, Jaromir, Westermann, Hannes, Benyekhlef, Karim, Alexander, Charlotte S., Grant, Jayla C., Amariles, David Restrepo, Hamdani, Rajaa El, Meeùs, Sébastien, Araszkiewicz, Michał, Ashley, Kevin D., Ashley, Alexandra, Branting, Karl, Falduti, Mattia, Grabmair, Matthias, Harašta, Jakub, Novotná, Tereza, Tippett, Elizabeth, Johnson, Shiwanni
Publikováno v:
In Proceedings of ICAIL 2021, pp. 129-138. 2021
In this paper, we examine the use of multi-lingual sentence embeddings to transfer predictive models for functional segmentation of adjudicatory decisions across jurisdictions, legal systems (common and civil law), languages, and domains (i.e. contex
Externí odkaz:
http://arxiv.org/abs/2112.07882
We analyze the ability of pre-trained language models to transfer knowledge among datasets annotated with different type systems and to generalize beyond the domain and dataset they were trained on. We create a meta task, over multiple datasets focus
Externí odkaz:
http://arxiv.org/abs/2112.07870
Publikováno v:
Frontiers in Artificial Intelligence and Applications, Volume 322: Legal Knowledge and Information Systems, 2019, pp. 123 - 132
In this paper, we present a method of building strong, explainable classifiers in the form of Boolean search rules. We developed an interactive environment called CASE (Computer Assisted Semantic Exploration) which exploits word co-occurrence to guid
Externí odkaz:
http://arxiv.org/abs/2112.05807