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"Legal information"
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On the concept of relevance in legal information retrieval
The concept of ‘relevance’ is crucial to legal information retrieval, but because of its intuitive understanding it goes undefined too easily and unexplored too often. We discuss a conceptual framework on relevance within legal information retrieval, based on a typology of relevance dimensions used within general information retrieval science, but tailored to the specific features of legal information. This framework can be used for the development and improvement of legal information retrieval systems.
Journal Article
Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model
Access to legal information is fundamental to access to justice. Yet accessibility refers not only to making legal documents available to the public, but also rendering legal information comprehensible to them. A vexing problem in bringing legal information to the public is how to turn formal legal documents such as legislation and judgments, which are often highly technical, to easily navigable and comprehensible knowledge to those without legal education. In this study, we formulate a three-step approach for bringing legal knowledge to laypersons, tackling the issues of navigability and comprehensibility. First, we translate selected sections of the law into snippets (called CLIC-pages), each being a small piece of article that focuses on explaining certain technical legal concept in layperson’s terms. Second, we construct a Legal Question Bank, which is a collection of legal questions whose answers can be found in the CLIC-pages. Third, we design an interactive CLIC Recommender. Given a user’s verbal description of a legal situation that requires a legal solution, CRec interprets the user’s input and shortlists questions from the question bank that are most likely relevant to the given legal situation and recommends their corresponding CLIC pages where relevant legal knowledge can be found. In this paper we focus on the technical aspects of creating an LQB. We show how large-scale pre-trained language models, such as GPT-3, can be used to generate legal questions. We compare machine-generated questions against human-composed questions and find that MGQs are more scalable, cost-effective, and more diversified, while HCQs are more precise. We also show a prototype of CRec and illustrate through an example how our 3-step approach effectively brings relevant legal knowledge to the public.
Journal Article
Sustaining the open-access regime through the legal information institutes: A success story
In the past decade, the provision of open access to legal information has experienced unprecedented improvement. One of the recent additions to the open-access regime is the introduction of Legal Information Institutes (LIIs) that provide access to important information contained in government gazettes, reports, judicial decisions, and statutory instruments. As important as LIIs are for enhancing legal research, a perusal of the relevant literature reveals that there is generally a dearth about LIIs.
Against this background, this article examines the LIIs' contribution to legal information to determine how to strengthen LIIs in terms of their content and viability.
Journal Article
Towards an open and decentralized case law curation ecosystem
Case law is the term that refers to reports of past court decisions. It is considered an essential source of law, vital for legal professionals. Existing case law services are currently centralized, with an entity having complete control over the data and often charging fees for its access and other adding value services. This paper attempts to leverage the potential of blockchain technology in order to develop a public and decentralized platform that allows the submission of court decisions in a decentralized database and employs a network of curators who offer their validation, classification, and evaluation. Specifically, we design, analyze and implement AnyCase, a proof-of-concept prototype system on the Ethereum platform. We focus on the establishment of a sybil-resistant voting protocol used for reaching agreement and the development of a tokenized economy that incentivizes participation. Our preliminary analysis indicates that, besides being decentralized, AnyCase has the potential to compete with existing centralized systems in several other aspects.
Journal Article
REVOLUTIONIZING ACCESS TO JUSTICE: THE ROLE OF AI-POWERED CHATBOTS AND RETRIEVAL-AUGMENTED GENERATION IN LEGAL SELF-HELP
According to the latest justice gap study by the Legal Services Corporation (LSC), 92% of low-income Americans face substantial civil legal problems without sufficient legal assistance, 75% experience at least one such problem annually, and 39% face five or more.1 Everyday legal needs include housing, education, healthcare, income, and safety situations.2 While LSC-funded organizations support those who qualify-U.S. citizens residing in households at or below 125% of the federal poverty guidelines3-millions confront their legal issues alone or simply do not take any action. [...]when the information sought is beyond the scope of data the model is trained on, it tends to fabricate facts and provide inaccurate information.14 In the legal field, this limitation has resulted in many disciplinary measures against attorneys and pro se litigants who submitted made-up cases in court. Improving the Accuracy of AI Chatbots Using Retrieval-Augmented Generation Several methods are available to improve the performance of LLMs, with one efficient approach being the training of LLMs using larger datasets and undergoing extensive fine-tuning.17 Fine-tuning involves training the model with domain-specific datasets to enhance its task-specific performance.18 While this technique can lead to better performance, it requires substantial data, computing power, and specialized technical expertise.19 Another drawback of fine-tuning is that it is not suitable for applications that require a more frequent iteration and the addition of new sources of knowledge.20 One method through which AI chatbots can improve their response accuracy is RAG. What's my next move here? * Protection order against stalking and harassment does not require an existing relationship between the parties. * Harassment includes any act where the adverse party threatens to harm another person in the future, damages another person's property. * You can use the online guided interview to generate the necessary court documents. * You are an AI assistant developed solely for guiding users through Nevada's court system. * Always provide links to additional resources when available. * If you detect that the user is afraid for their safety or life or this is an emergency situation, always tell them to call 911.
Journal Article
Enhanced Retrieval-Augmented Generation Using Low-Rank Adaptation
Recent advancements in retrieval-augmented generation (RAG) have substantially enhanced the efficiency of information retrieval. However, traditional RAG-based systems still encounter challenges, such as high latency in output decision making, the inaccurate retrieval of road traffic-related laws and regulations, and considerable processing overhead in large-scale searches. This study presents an innovative application of RAG technology for processing road traffic-related laws and regulations, particularly in the context of unmanned systems like autonomous driving. Our approach integrates embedding generation using a LoRA-enhanced BERT-based uncased model and an optimized retrieval strategy that combines maximal marginal similarity score thresholding with contextual compression retrieval. The proposed system enhances and achieves improved retrieval accuracy while reducing processing overhead. Leveraging road traffic-related regulatory datasets, the LoRA-enhanced model demonstrated remarkable performance gains over traditional RAG methods. Specifically, our model reduced the number of trainable parameters by 13.6% and lowered computational costs by 18.7%. Performance evaluations using BLEU, CIDEr, and SPICE scores revealed a 4.36% increase in BLEU-4, a 6.83% improvement in CIDEr, and a 5.46% improved in SPICE, confirming greater structural accuracy in regulatory text generation. Additionally, our method achieved an 8.5% improvement in retrieval accuracy across key metrics, outperforming baseline RAG systems. These contributions pave the way for more efficient and reliable traffic regulation processing, enabling better decision making in autonomous systems.
Journal Article
Artificial Intelligence Applied in Legal Information: A Systematic Mapping Study
In the context of the digital era, our lives have become more dynamic, and the development of advanced technologies is transforming the way civil law relationships are handled. Today, legal professionals commonly use software that accelerates daily processes through the application of artificial intelligence technologies. Legal Information Retrieval is a significant and challenging field of AI that focuses on finding and analyzing legal norms and documents relevant to a user’s information needs. The objective of this article is to identify and synthesize the main approaches, trends, and advances in the application of AI in Legal Information Retrieval. Through a review of recent research, this study aims to provide a clear overview of the strategies used, methodologies employed, and emerging areas of focus. To achieve these objectives, exhaustive search methods were applied to academic databases and repositories, selecting relevant studies published over the past twelve years. In the first stage, 2307 articles were found. Then, in the second stage, 354 technical articles were selected after applying inclusion/exclusion criteria. Finally, after a third round of filtering, 18 articles were selected for final analysis. In addition to the inclusion-exclusion criteria, the articles underwent a process of analysis and classification based on themes, methods, and results. The findings reflect a growing interest in the application of AI techniques in Legal Information Retrieval. The approaches identified focus on improving the relevance of search results and automating legal processes. There is also a progressive adoption of Natural Language Processing and machine learning techniques. Finally, a panoramic view is provided of the intersection between AI and Legal Information Retrieval. The results highlight the importance and potential of AI techniques in the legal field, while underscoring the need for deeper research and integrated approaches to address the specific challenges of Legal Information Retrieval in a technologically dynamic world.
Journal Article