REINFORCEMENT LEARNING ALIGNMENT OF LLM FOR LEGAL CHATBOT AN LLM-BASED APPROACH TO INDIAN LAW ADHERENCE

Abstract

Answering legal questions is complex because such documents vary, and their application is complex. Unlike facts and figures, accurate responses frequently requires relevant legal knowledge, which is challenging even for professionals. Question answering (QA) systems are designed to answer questions open-ended question, as they are asked in daily language. They adopt methods such as natural language processing to comprehend the asked questions and consequently search for information in order to get the right answer. Currently, one is unable to come across many surveys that are comprehensive in analyzing how these systems deal with legal issues. Legal document review and analysis have been revolutionized by Large Language Models (LLMs). Lawyers frequently encounter lengthy and complex contracts and agreements that require a significant amount of time. By pulling out key information automatically, summarizing it, locating relevant sections, pointing out potential problems (and even explaining them), LLMs accelerate the document review process; thus leaving lawyers more time for other legal tasks like analysis, strategy formulation or negotiation. The advent of Large Language Models (LLMs) raises serious ethical and regulatory concerns. These models are made from lots of data, some of which might be biased. If not checked, LLMs could amplify or reproduce existing injustices. This means that there needs to be careful control to ensure that the LLM’s outputs stay true. There is also the issue of intellectual property, particularly infringement on copyright in the data used for training and ownership of content produced by such systems. These issues are being tackled by courts and governments globally but it is still unclear how this area will change.