Exploring the State of the Art in Legal QA Systems

Answering questions related to the legal domain is a complex task, primarily due to the intricate nature and diverse range of legal document systems. Providing an accurate answer to a legal query typically necessitates specialized knowledge in the relevant domain, which makes this task all the more challenging, even for human experts. QA (Question answering systems) are designed to generate answers to questions asked in human languages. They use natural language processing to understand questions and search through information to find relevant answers. QA has various practical applications, including customer service, education, research, and cross-lingual communication. However, they face challenges such as improving natural language understanding and handling complex and ambiguous questions. Answering questions related to the legal domain is a complex task, primarily due to the intricate nature and diverse range of legal document systems. Providing an accurate answer to a legal query typically necessitates specialized knowledge in the relevant domain, which makes this task all the more challenging, even for human experts. At this time, there is a lack of surveys that discuss legal question answering. To address this problem, we provide a comprehensive survey that reviews 14 benchmark datasets for question-answering in the legal field as well as presents a comprehensive review of the state-of-the-art Legal Question Answering deep learning models. We cover the different architectures and techniques used in these studies and the performance and limitations of these models. Moreover, we have established a public GitHub repository where we regularly upload the most recent articles, open data, and source code. The repository is available at: \url{https://github.com/abdoelsayed2016/Legal-Question-Answering-Review}

A Survey on Legal Question Answering Systems

Many legal professionals think that the explosion of information about local, regional, national, and international legislation makes their practice more costly, time-consuming, and even error-prone. The two main reasons for this are that most legislation is usually unstructured, and the tremendous amount and pace with which laws are released causes information overload in their daily tasks. In the case of the legal domain, the research community agrees that a system allowing to generate automatic responses to legal questions could substantially impact many practical implications in daily activities. The degree of usefulness is such that even a semi-automatic solution could significantly help to reduce the workload to be faced. This is mainly because a Question Answering system could be able to automatically process a massive amount of legal resources to answer a question or doubt in seconds, which means that it could save resources in the form of effort, money, and time to many professionals in the legal sector. In this work, we quantitatively and qualitatively survey the solutions that currently exist to meet this challenge.Comment: 57 pages, 1 figure, 10 table

Exploring The Relationship Between Academic Dishonesty And Moral Development In Law School Students

This mixed methods study explored whether a relationship existed between moral development and dishonest academic behaviors in law students. The quantitative portion of the study utilized a survey adapted from James Rest\u27s Defining Issues Test and Donald McCabe\u27s Academic Integrity Survey. Law students were solicited by email from two public institutions. The usable sample included 134 law students in the first, second, and third years of law school. Qualitatively, a law school honor council chair was intervieas part of a case study. The transcript was coded and explored for themes and emerging topics. In tandem, the quantitative and qualitative aspects work together to provide a framework with which to guide practitioners in law school teaching and administration. This study shono relationship between the moral aptitude and academic dishonesty of law students. Also, no relationship existed between moral aptitude and category (papers, assignments and homework, or exams) of dishonest academic behavior. However, the study revealed that the highest number of instances of dishonest academic behavior occurred when students work on assignments or homework for class. Reference to materials, such as the internet, other law students or attorneys, or print materials, were consulted even when expressly prohibited by law professors. The study also indicated that the moral development of law students is declining. The P-scores of this study\u27s participants was 35.5. Compared to their counterparts in the 1960s, 70s, and 80s, the postconventional scores of today\u27s law student is equivalent to high school and undergraduate students then. Studies show that students completing a clinical requirement in law school experience higher moral development scores. This is something law schools may want to consider going forward if moral development is vital to its institutional mission. Qualitatively, the case study provided useful guidance when dealing with academic dishonesty and the formation of an honor code from a law student\u27s perspective. More dialogue is needed between an institution\u27s honor council and the faculty/administration. This ensures that everyone is working with the same information and provides consistent communication to the law school community at large

The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie

In the digital era, the integration of artificial intelligence (AI) in education has ushered in transformative changes, redefining teaching methodologies, curriculum planning, and student engagement. This review paper delves deep into the rapidly evolving landscape of digital education by contrasting the capabilities and impact of OpenAI's pioneering text generation tools like Bing Chat, Bard, Ernie with a keen focus on the novel ChatGPT. Grounded in a typology that views education through the lenses of system, process, and result, the paper navigates the multifaceted applications of AI. From decentralizing global education and personalizing curriculums to digitally documenting competence-based outcomes, AI stands at the forefront of educational modernization. Highlighting ChatGPT's meteoric rise to one million users in just five days, the study underscores its role in democratizing education, fostering autodidacticism, and magnifying student engagement. However, with such transformative power comes the potential for misuse, as text-generation tools can inadvertently challenge academic integrity. By juxtaposing the promise and pitfalls of AI in education, this paper advocates for a harmonized synergy between AI tools and the educational community, emphasizing the urgent need for ethical guidelines, pedagogical adaptations, and strategic collaborations

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Large language models (LLMs) are a special class of pretrained language models obtained by scaling model size, pretraining corpus and computation. LLMs, because of their large size and pretraining on large volumes of text data, exhibit special abilities which allow them to achieve remarkable performances without any task-specific training in many of the natural language processing tasks. The era of LLMs started with OpenAI GPT-3 model, and the popularity of LLMs is increasing exponentially after the introduction of models like ChatGPT and GPT4. We refer to GPT-3 and its successor OpenAI models, including ChatGPT and GPT4, as GPT-3 family large language models (GLLMs). With the ever-rising popularity of GLLMs, especially in the research community, there is a strong need for a comprehensive survey which summarizes the recent research progress in multiple dimensions and can guide the research community with insightful future research directions. We start the survey paper with foundation concepts like transformers, transfer learning, self-supervised learning, pretrained language models and large language models. We then present a brief overview of GLLMs and discuss the performances of GLLMs in various downstream tasks, specific domains and multiple languages. We also discuss the data labelling and data augmentation abilities of GLLMs, the robustness of GLLMs, the effectiveness of GLLMs as evaluators, and finally, conclude with multiple insightful future research directions. To summarize, this comprehensive survey paper will serve as a good resource for both academic and industry people to stay updated with the latest research related to GPT-3 family large language models.Comment: Preprint under review, 58 page