Large Language Models in the Legal Domain
Generative AI is having a fundamental impact on industries with large amounts of textual data. Large language models (LLMs) enable legal professionals to accelerate workflow and uncover new insights in existing data repositories.
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Accelerate research and case analysis
Enhance contract review and drafting efficiency
Generate case summaries on demand
Streamline due diligence processes
Improve client communication
Automate routine legal tasks
Top AI-Driven Opportunities in the Legal Sector
Case Law
Lawyers can use LLMs to scan and analyze case law, find relevant precedents, and summarize key findings. These AI-powered tools help lawyers build stronger arguments and predict case outcomes. Internally, LLMs help legal professionals identify patterns across multiple jurisdictions and stay on top of evolving legal interpretations.
Due Diligence
In mergers and acquisitions, LLMs streamline the due diligence process by efficiently reviewing large volumes of contracts, financial documents, and regulatory filings. They can flag potential risks, inconsistencies, and compliance issues, significantly reducing the time and resources required for thorough assessments.
Patent Screening
LLMs help patent attorneys and examiners quickly analyze patent applications, identify prior art, and evaluate novelty and non-obviousness. These tools enable patent professionals to navigate complex technical descriptions and cross-reference massive patent databases. In addition, LLMs help draft patent claims, improve the quality and efficiency of patent prosecution, and reduce the risk of infringement.
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Frequently Asked Questions
In the age of AI models and distributed computing infrastructures, data security is a major concern - even more so in an area as sensitive as the law. At deepset, we understand this and have made data security a priority. Users can manage access with MFA and SSO in deepset Cloud, while our Virtual Private Cloud (VPC) option gives them the flexibility to keep their data layer in their preferred location. We are also SOC 2 and GDPR compliant and CSA STAR Level 1 certified.
While there are companies that have gone the route of building their own LLM from scratch - spending millions of dollars in the process - it is actually a better idea to remain flexible if you want to stay competitive. Vendor agnosticism, a principle championed by deepset, allows you to change models when they no longer serve you. For example, if a cheaper or faster model comes along, you can simply plug it into your existing pipeline and move on. Fine-tuning an existing model, on the other hand, can be a viable solution for adapting an LLM to the specific language of the legal domain. We're happy to share our years of experience in customizing models for different applications to help you build the solution that's perfect for your use case.
To build products or internal tools with AI, you need to put together a team that understands AI technology, has a product mindset, and understands both user needs and business requirements. This type of team is called an "AI team." It can be large or small, as long as it has the right cross-functional skills.
LLMs "hallucinate", that is, they make up facts that are not supported by any data. Because of the eloquence of these models, hallucinations can be difficult to detect, creating a volatile factor that is a barrier to using LLMs in production. However, using a combination of prevention techniques, teams can reduce the number of hallucinations to a minimum. These include effective prompting, grounding the LLM's response in fact-checked data through retrieval augmented generation (RAG), and monitoring the Groundedness of responses.
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