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Stéphane Cottin
onto Documentation juridique April 1, 2013 5:12 PM
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For centuries the traditional approach to library design has focused on allocating sufficient space for the collection, study space, and staff offices. In cont
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Ce guide méthodologique a été conçu pour fournir : • une cartographie des gisements d’informations disponibles pour toutes les sources du droit français, du droit de l’Union européenne et du droit international (législation, jurisprudence, doctrine), • une description plus détaillée des données et outils essentiels, accompagnée de trucs et astuces pour une utilisation efficace, • une méthode de travail adaptable à tous les types de recherche et des conseils plus pointus pour des requêtes et investigations spécialisées.Le corps du texte s’accompagne d’outils permettant d’acquérir des compétences en recherche d’information juridique : des copies d’écran illustrent les procédures, des résumés permettent de mémoriser les points essentiels, des focus approfondissent certains aspects plus complexes. Privilégiant la pratique et une approche la plus concrète possible, l’ouvrage devrait être utile à l’étudiant en licence ou master, au professionnel du droit ou de l’information juridique. Au-delà, il devrait intéresser toute personne souhaitant : • se former à la recherche documentaire juridique, • vérifier ou actualiser ses connaissances dans le domaine, • former des étudiants à ce type de recherche, • mesurer l’impact de l’intelligence artificielle sur la recherche d’information juridique.
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From
papers
The rapid advancement of artificial intelligence (AI) has had a profound impact on nearly every industry, including legal academia. As AI-driven tools like Chat
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From
papers
This article, based upon a keynote address by Professor Harry Surden, provides an in-depth overview of the recent advancements in artificial intelligence (AI) s
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From
papers
Organizations developing machine learning-based (ML) technologies face the complex challenge of achieving high predictive performance while respecting the law.
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From
papers
Holzenberger, Nils and Maxwell, Winston, A Quantitative Approach to the GDPR’s Anonymization and Pseudonymization Tests (December 06, 2024). Available at SSRN: https://ssrn.com/abstract=5162461 or http://dx.doi.org/10.2139/ssrn.5162461
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In this study, the ability of two free large language models (LLMs), GPT-4o mini and Bielik-11B-v2, to solve simple legal problems was tested. The general corre
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From
papers
Legal decision-making is heavily influenced by cognitive processes like memory and judgment, which are vulnerable to biases such as false autobiographical memor
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In The Gray Box of Legal Analysis, we argued that law faculty and students under appreciate the extent and quality of prior legal knowledge needed for practice
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From
papers
This article, based upon a keynote address by Professor Harry Surden, provides an in-depth overview of the recent advancements in artificial intelligence (AI) s
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From
papers
Rapid advances in artificial intelligence (AI) have ushered in an era where machines meaningfully assist—and in some cases independently produce—legal reasoning
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From
dassignies
La recherche juridique se trouve aujourd'hui dans une phase de transition significative, marquée par une succession d'approches méthodologiques dont l'évolution reflète les progrès technologiques en matière de traitement de l'information. Initialement fondée sur des systèmes statistiques rudimentaires – reposant essentiellement sur la concordance lexicale et la fréquence d'occurrence des termes – cette recherche a progressivement intégré des dimensions sémantiques avec l'avènement des modèles vectoriels de type word2vec, capables d'appréhender les relations conceptuelles entre les termes juridiques.
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From
papers
Campagnolo, Yan and van Haeren, Leonie and Ravitharan, Shaarini and Murray, Emma and Barrera, Ephraim, Incidence de l’intelligence artificielle sur les revues de droit (Artificial Intelligence’s Impact on Legal Journals) (September 30, 2024). Ottawa Faculty of Law Working Paper No. 2025-05, Available at SSRN: https://ssrn.com/abstract=5190282 or http://dx.doi.org/10.2139/ssrn.5190282
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From
papers
Rapid advances in artificial intelligence (AI) have ushered in an era where machines meaningfully assist—and in some cases independently produce—legal reasoning
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![[2505.01944] Explainability by design: an experimental analysis of the legal coding process | Documentation juridique | Scoop.it](https://img.scoop.it/R3Nf-aV-zFCPDr3s1jV_-zl72eJkfbmt4t8yenImKBVvK0kTmF0xjctABnaLJIm9)
From
arxiv
Behind a set of rules in Deontic Defeasible Logic, there is a mapping process of normative background fragments. This process goes from text to rules and implicitly encompasses an explanation of the coded fragments. In this paper we deliver a methodology for \textit{legal coding} that starts with a fragment and goes onto a set of Deontic Defeasible Logic rules, involving a set of \textit{scenarios} to test the correctness of the coded fragments. The methodology is illustrated by the coding process of an example text. We then show the results of a series of experiments conducted with humans encoding a variety of normative backgrounds and corresponding cases in which we have measured the efforts made in the coding process, as related to some measurable features. To process these examples, a recently developed technology, Houdini, that allows reasoning in Deontic Defeasible Logic, has been employed. Finally we provide a technique to forecast time required in coding, that depends on factors such as knowledge of the legal domain, knowledge of the coding processes, length of the text, and a measure of \textit{depth} that refers to the length of the paths of legal references.
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Factors are a foundational component of legal analysis and computational models of legal reasoning. These factor-based representations enable lawyers, judges, a
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papers
Factors are a foundational component of legal analysis and computational models of legal reasoning. These factor-based representations enable lawyers, judges, a
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À l’ère du numérique, l’intelligence artificielle (IA) s’impose comme un levier majeur de transformation dans de nombreux secteurs. Si ses apports sont incontestables, cette avancée suscite de sérieuses interrogations quant au respect des droits fondamentaux, notamment en matière de non-discrimination. Derrière l’apparente objectivité des algorithmes, subsiste un risque réel de reproduction, voire d’amplification, des biais […]
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Lawyers want genAI tools to give good results. But, what happens when they become almost perfect, i.e. 99.9% accurate, all of the time? That would mark the end of the legal world as we know it. So …
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From
papers
Rapid advances in artificial intelligence (AI) have ushered in an era where machines meaningfully assist—and in some cases independently produce—legal reasoning
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From
arxiv
With the surge of ChatGPT,the use of large models has significantly increased,rapidly rising to prominence across the industry and sweeping across the internet. This article is a comprehensive review of fine-tuning methods for large models. This paper investigates the latest technological advancements and the application of advanced methods in aspects such as task-adaptive fine-tuning,domain-adaptive fine-tuning,few-shot learning,knowledge distillation,multi-task learning,parameter-efficient fine-tuning,and dynamic fine-tuning.
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We propose the task of legal question generation (QG) as an application in Legal NLP. Specifically, the task is to generate a question, given a context and an optional keyword. We create the first dataset for the QG task in the legal domain, called LegalQ, consisting of 2023 pairs spanning the legal systems of multiple countries, and multiple languages. We then use this dataset to benchmark several Large Language Models (LLMs) including Turbo-GPT-3.5, GPT-4, Llama2-70b, Llama2-13b, and Aalap-Mistral-7b (a legal domain-specific LLM). We also fine-tune several open-source LLMs such as T5, BART, Pegasus, and Flan-T5, which helps to improve results over zero-shot prompting of LLMs. We also use the idea of in-context learning (via few-shot examples) to generate questions of varying types and difficulty levels. Furthermore, we introduce a novel domain-specific prompting strategy based on chain-of-thoughts prompting for question generation. Further, we perform Bloom Taxonomy analysis of the questions generated by the LLMs, thereby showing that ‘understanding’ and ‘remembering’ are the two most dominant types of questions generated by the LLMs. Human evaluation of the generated questions shows promise in terms of generating grammatically correct, relevant, appropriate, complex, and novel questions. Finally, we analyze the incomplete or unanswerable generated questions to find possible reasons for these issues.
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papers
Can large language models (LLMs) serve as "AI judges" that provide answers to legal questions? I conduct the first series of empirical experiments to
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Presentato in Parlarmento il Manifesto per avere leggi “machine-readable”. L’intelligenza artificiale entra progressivamente nel cuore dei sistemi normativi, tra promesse di efficienza e rischi di standardizzazione
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papers
This paper aimed to analyze the role Artificial intelligence in streamlining of process, improve research and analysis of facts and findings of cases and judgme
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papers
Recent breakthroughs in statistical language modeling have impacted countless domains, including the law. Chatbot applications such as ChatGPT, Claude, and Deep
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papers
This report provides an overview of the opportunities and challenges presented by the use of artificial intelligence (AI) and its impact on legal journals, incl
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