Revolutionizing Legal Research: The Power of Large Language Models and Natural Language Search
Insights and information are paramount to effective legal work. Being able to access information quickly helps law firms deliver high quality services, and the faster, more accurate and relevant this information is, the better.
Accessing information, however, is not always easy, even in the most well maintained knowledge base. They can be a maze to navigate for the uninitiated, filled with a variety of statutes and case law to contracts and internal documents, and often using sub-par search tools.
Fortunately, in this article, we will talk you through the challenges facing information systems professionals in legal firms, and explore how generative AI creates brand new opportunities to leverage natural language search. This will help you to boost productivity, searchability and make greater value of your existing insights.
It’s more than search–it’s about surfacing important information at the right time
Desk research encompasses a wide array of search activities, from verifying legal precedents and exploring regulatory frameworks to conducting due diligence and compiling client advisories.
The quality and efficacy of desk research directly impact a firm's ability to provide informed counsel, mitigate risks, and navigate the complexities of legal proceedings.
Search is a critical part of accessing information during research. Without search, sourcing the information that informs strategic decisions, case strategies, ensures compliance and keeps your work standards high can become nigh-on impossible.
Many law firms have various platforms and databases housing this valuable information. Each has its own, unique interface, search experience, and query requirements. Firms often lack a single universal search tool can make finding information cumbersome, time-consuming, and exhausting.
It’s time to look beyond traditional search
Traditionally, legal tools provided a type of search logic called Boolean. A Boolean search uses the operators of AND, OR and NOT, to narrow down results based on the keywords it is given.
This method requires precise language – often matching the exact terminology used by documents – and therefore an intricate understanding of the files, information and organisation system used. This presents problems for legal professionals, who spend considerable time formulating queries, interpreting results, and cross-referencing information across different systems, from online legal databases to internal archives.
Large language models (LLMs) make search more expressive and natural
An LLM is an advanced AI system trained on vast amounts of text data to understand, generate, and interpret language in a way that mimics human-like understanding. This technology can process and produce text in response to prompts, making it an invaluable tool for tasks requiring natural language comprehension and generation, including translation, summarisation, and, notably, natural language search.
Unlike traditional search engines that require specific keywords or Boolean search techniques, LLMs can interpret queries expressed in natural, conversational language. This capability allows legal professionals to ask complex questions directly and receive concise, relevant answers or documents.
A search for 'womens rights' case law using WorkflowGPT's Semantic Search feature.
The advent of natural language search (NLS) technologies represents a paradigm shift in legal research. By leveraging the power of artificial intelligence and natural language processing, NLS allows legal professionals to conduct searches using plain, conversational language. This innovation addresses the challenges of traditional legal searches by simplifying access to information, enhancing the efficiency of research processes, and democratising knowledge throughout your organisation.
Data security and secure knowledge base connectivity
For LLMs to be truly effective in legal research, they must be securely connected to the firm's knowledge bases. This connection enables LLMs to access and analyse the specific, proprietary information that law firms rely on, rather than generic data prone to inaccuracies or "hallucinations" (false information generated by AI).
Connecting LLMs securely to a firm's databases ensures that the search results are accurate, relevant and tailored to the specific needs of the legal team.
To mitigate the risks associated with AI-generated content, such as misinformation or data privacy concerns, implementing AI safety and safeguarding measures is paramount.
Conclusion
By moving beyond the limitations of traditional Boolean search techniques, LLMs offer an intuitive, efficient, and contextually aware method of querying vast legal databases using natural language. This advancement not only streamlines the research process but also democratises access to complex legal information, making it more accessible to professionals at all levels of expertise.
However, the effectiveness of using LLMs in legal research is contingent upon their secure connection to law firms' proprietary knowledge bases. This connection ensures that the search results are directly relevant to the firm's specific needs and are based on accurate, up-to-date information. Moreover, implementing rigorous AI safety and safeguarding measures is critical to protecting the confidentiality of sensitive data and maintaining the trust of clients.
