The integration of Artificial Intelligence(AI) into the legal profession raises significant questions about the capacity of Large Language Models(LLM) to perform key legal tasks. In this paper, I empirically evaluate how well LLMs, such as GPT, Claude, and Llama, perform key legal tasks in the Indian context, including issue spotting, legal drafting, advice, research, and reasoning. Through a survey experiment, I compare outputs from LLMs with those of a junior lawyer, with advanced law students rating the work on helpfulness, accuracy, and comprehensiveness. LLMs excel in drafting and issue spotting, often matching or surpassing human work. However, they struggle with specialised legal research, frequently generating hallucinations, factually incorrect or fabricated outputs. I conclude that while LLMs can augment certain legal tasks, human expertise remains essential for nuanced reasoning and the precise application of law.

Traditionally in the domain of legal research, the retrieval of pertinent citations from intricate case descriptions has demanded manual effort and keyword-based search applications that mandate expertise in understanding legal jargon. Legal case descriptions hold pivotal information for legal professionals and researchers, necessitating more efficient and automated approaches. We propose a methodology that combines natural language processing (NLP) and machine learning techniques to enhance the organization and utilization of legal case descriptions. This approach revolves around the creation of textual embeddings with the help of state-of-art embedding models. Our methodology addresses two primary objectives: unsupervised clustering and supervised citation retrieval, both designed to automate the citation extraction process. Although the proposed methodology can be used for any dataset, we employed the Supreme Court of The United States (SCOTUS) dataset, yielding remarkable results. Our methodology achieved an impressive accuracy rate of 90.9%. By automating labor-intensive processes, we pave the way for a more efficient, time-saving, and accessible landscape in legal research, benefiting legal professionals, academics, and researchers.

We present the Legal Passage Retrieval Dataset LePaRD. LePaRD is a massive collection of U.S. federal judicial citations to precedent in context. The dataset aims to facilitate work on legal passage prediction, a challenging practice-oriented legal retrieval and reasoning task. Legal passage prediction seeks to predict relevant passages from precedential court decisions given the context of a legal argument. We extensively evaluate various retrieval approaches on LePaRD, and find that classification appears to work best. However, we note that legal precedent prediction is a difficult task, and there remains significant room for improvement. We hope that by publishing LePaRD, we will encourage others to engage with a legal NLP task that promises to help expand access to justice by reducing the burden associated with legal research. A subset of the LePaRD dataset is freely available and the whole dataset will be released upon publication.

In the words of former GE CEO, Jack Welch, "If you don't have a competitive advantage, don't compete." This CLE covers how technology has changed the practice of law and how we can (and should) use analytics to our transactional and litigation advantage. Examine how analytics have changed our application of model rules of professional responsibility. Understand how analytics and artificial intelligence are applied in both professional and legal world. Examine how to use and apply analytics in a legal case.

Artificial intelligence tools have become prevalent in legal practice, particularly in eDiscovery. That doesn't mean, however, that law firms and litigation support teams have been quick to embrace them. Despite their benefits, many legal organizations have been hesitant to implement AI tools. In the ABA 2020 Legal Tech Survey, 23% of law firms reported not being interested in AI, while 34% said they didn't know enough about AI to speak to their firms' interest. While the survey showed that larger firms were more likely to adopt AI tools, that leaves a lot of room for smaller firms to use AI to their advantage.

Imagine what a lawyer does on a given day: researching cases, drafting briefs, advising clients. While technology has been nibbling around the edges of the legal profession for some time, it's hard to imagine those complex tasks being done by a robot. And it is those complicated, personalized tasks that have led technologists to include lawyers in a broader category of jobs that are considered pretty safe from a future of advanced robotics and artificial intelligence. But, as we discovered in a recent research collaboration to analyze legal briefs using a branch of artificial intelligence known as machine learning, lawyers' jobs are a lot less safe than we thought. It turns out that you don't need to completely automate a job to fundamentally change it.

Over the past five years, artificial intelligence (AI) and machine learning in marketing have come a long way. Yet many marketers who are using today's programmatic and social platforms to reach their audience are not exploiting the full power of the algorithms...

Artificial intelligence (AI), the simulation of human intelligence in machines, is changing the face of the legal industry. Law firms globally are apprehensive about what lies ahead for the industry but changes are already in motion and there is no escape, says Dr Anton Ravindran. Anton is the CEO of SmartLaw, a start-up based in Singapore that utilises AI to assist lawyers in their work. Its online services help lawyers scan through thousands of pages of documents and answer their law-related questions in seconds. It also helps them predict sentencing outcomes, and extract legal precedents and verdicts almost instantly for criminal offence, contested divorce and medical negligence.

Both Lexis and Lexis demonstrate that artificial intelligence-powered legal research is a game-changer for lawyers and their firms, helping them find relevant information faster, more efficiently and cost-effectively. Artificial intelligence (AI), of course, refers to computer software and systems that rather than only relying on pre-programmed tasks, learn, plan, reason and process natural language as they go. For years now, we've incorporated AI-powered features into our legal research platforms to drive better insights in a user-friendly way, reveal previously unknowable connections in the data and incorporate real-time developments in the law. Simply stated, AI-powered legal research platforms can help lawyers do more billable work more quickly, allowing them to spend more time putting that research to good use by counseling clients, negotiating with opposing counsel or performing other higher-level work. This is particularly important for attorneys who provide their services on a flat-fee or contingency-fee basis, where more time spent on legal research could lead to lower profit margins.

A quick search of recent headlines and blog posts suggests there is anxiety surrounding artificial intelligence (AI). One article shouts, "Robots will soon do your Taxes!" Another reads, "Lawyers could be the next profession to be replaced by computers." Those of us involved in technology marketing strategy and communications are struggling to understand what the true impact of AI will be on our respective companies and clients, and on the technology-based products and services they provide. New AI applications in legal research, contracts management, or e-discovery may fundamentally change the value proposition. For those AI solutions, marketers and communications teams must strive to effectively educate prospects and customers on the nature of artificial intelligence, separating the rumors from facts.