by Jon Fowler

This article first appeared in Legaltech News on November 27, 2024.

Large language models (LLMs) have ushered in a new era of legal practice, significantly disrupting traditional dispute resolution methodologies. Beyond their application in enhancing legal research and e-discovery, they are poised to revolutionize alternative dispute resolution (ADR) mechanisms.

Revolutionizing Legal Research, LLMs in Practice

One of the most prominent applications of LLMs in litigation is their ability to augment legal research. By analysing vast datasets of legal materials, they can swiftly identify relevant case law, statutes, and other pertinent legal documents, thereby saving legal practitioners valuable time and effort. Furthermore, they can summarize complex legal concepts and arguments concisely and understandably, facilitating effective communication between legal professionals and their clients.

In e-discovery, LLMs have emerged as indispensable document review and analysis tools. These models can use natural language processing techniques to identify key documents and extract relevant information from extensive datasets. This significantly reduces the time and cost associated with manual document review, enabling legal professionals to concentrate on more strategic aspects of the case.

Specific applications of LLMs in e-discovery platforms include natural language “ask” scenarios, where when trained on a specific e-discovery dataset, they can answer questions posed in natural language. For instance, a lawyer could ask, “What happened in the California gas markets in the early 2000s?” The platform would provide a comprehensive English-language summary, referencing the relevant documents.

Another application involves “briefing” the LLM to categorize large sets of documents based on predetermined issue tags. The model will not only tag the documents but also concisely explain their categorization. While there were initial concerns about the reliability of machine-driven document review, LLMs are increasingly demonstrating their ability to perform this task effectively.

Moreover, LLMs enhance privilege review accuracy and efficiency. By analysing document content and identifying potentially privileged information, these models can assist legal professionals in safeguarding sensitive client communications.

Overcoming Key Challenges in LLM Adoption

Despite their numerous benefits, LLMs have limitations. One of the most significant challenges is the risk of hallucinations, where they may generate incorrect or misleading information. To mitigate this risk, it is imperative to carefully evaluate the outputs and cross-reference them with other sources of information.

Another challenge is ensuring the transparency and accountability of LLM usage. While they can provide explanations for their decisions, understanding the underlying algorithms and data on which they are trained is essential. This will help ensure that they are used fairly and equitably.

A consistent and robust approach is essential to maximizing the benefits of LLM technology in dispute resolution. This includes developing clear guidelines for their use, providing adequate training to legal professionals, and continuously monitoring and evaluating the performance of LLM-based systems.

Enhancing Efficiency and Reducing Costs With LLMs

A significant challenge for current applications of LLMs in e-discovery is the cost and efficiency of processing extensive datasets. The iterative nature of these applications can be computationally expensive when dealing with massive volumes of data. Various chunking methodologies and optimized models are being explored to enhance efficiency and reduce costs. Chunking, a technique employed in generative AI, involves breaking down enormous datasets into smaller, more manageable segments. This approach is particularly beneficial for LLMs and semantic retrieval systems, as it can improve computational efficiency, memory utilization, and the accuracy of results.

Shaping the Future of Dispute Resolution With LLMs

The potential applications of LLMs in dispute resolution are vast and ever-expanding. In the future, we may witness LLMs constructing legal arguments, predicting case outcomes, drafting documents, and even serving as AI mediators or arbitrators. In mediation, we could see them providing neutral analysis and being used to draw on past case outcomes to streamline the process. For smaller disputes, AI-powered systems could handle most of the process, reducing costs and increasing access to justice. In courtrooms, LLMs could provide live summaries and analysis, saving time for all parties involved.

As LLMs continue to evolve, legal professionals must stay informed about the latest developments and explore how they can leverage these technologies to improve the efficiency and effectiveness of dispute resolution processes. By embracing innovation and adapting to the changing landscape of legal practice, lawyers can position themselves at the forefront of the legal industry.