Introduction

Lawyers have infamously known to be laggards when we talk about anything with the word technology in it. One can argue that the aversion to technology is not unfounded, for this an occupation that is fundamentally dictated by codification, precedence and tempered by time. So how have Generative AI and LLM’s, a fairly recent development in the field of technology, managed to influence this shift towards digital transformation

What Is Generative Ai And Llm’s?

Artificial Intelligence has been around for a while but Generative AI and LLMs are currently talk of the town. Touted to be revolutionary, Generative AI and LLMs have the ability to mimic human creativity by generating realistic and original outputs. Even though this seems like a magician conjuring tricks, the technology behind it is fairly easy to comprehend.

Generative artificial intelligence is nothing but a model designed to “learn” from the data that it was trained on, to generate a novel output that reflects the characteristics of the training data but does not duplicate it. Generative AI can be used to produce a variety of novel content, such as text, images, video, speech, music and product designs.

LLMs (Large language models) constitute a specific category of generative AI models with a specialized focus on text-based data and human language modelling. These models are trained on large amounts of text data and learn the statistical properties of language. They have the ability to predict patterns and what comes next in a given sequence of words or generating text based on a prompt.

This ability, understandably can open up a wide avenue of use cases for the legal sector, some of them include:

Legal Research

Generative AI can be used to scour through large legal databases and find relevant information be it statutes, precedents and case laws efficiently.

Further the technology is not only capable of searching databases and providing relevant results, but is also capable of providing insightful summaries for the same, which can save up time by enabling professionals to focus on analysing and building strategy.

Compliance and Regulatory Monitoring

Another use case is that this technology could also be used to monitor regulatory changes in real time and identify compliance requirements across jurisdictions. This could potentially highlight areas of non-compliance, and provide recommendations on necessary actions, to ensure compliance and stay up to date with changing regulatory landscapes. Moreover, real time identification of any non- compliance can not only save time but could also save money and human effort.

Contract Analysis and Review

By training Generative AI systems on vast datasets of contracts, organizations can harness the technology to generate customised contracts that precisely meet their needs. They can automate the process of drafting routine clauses by generating templates and populating them with relevant information.

The system relies on learned patterns to create contracts, enhancing consistency and accuracy. Further it can also be trained to ensure that the contracts abide by internal company policies and statutory compliances by providing automatic compliance checks that are updated in real time. This automated drafting process not only saves valuable time but also minimizes the risk of human error.

Generative AI and LLM tools can also review contracts to identify important clauses, flag potential issues, and suggest revisions based on best practices and company policies, simplifying the negotiation process and reducing the risk of disputes.

Due Diligence

Generative AI can identify potential legal risks and issues, and generating due diligence reports by reviewing large volumes of documents for corporate transactions.

Contract and Intellectual Property Management

Managing contracts and extensive portfolios of Intellectual Property has always been cumbersome. However effective management of contracts throughout their lifecycle can be achieved with generative AI and LLM’s. This includes contract deadline tracking, renewal management and compliance. With respect to Intellectual property, Generative AI can help in the process of IP analysis, trademark searches, and infringement detection.

A recent Thomson Reuters survey has found that those in corporate law departments are largely optimistic about the potential for generative AI and programs such as Chat GPT in performing both legal and non-legal work. “In total, 82% of respondents say generative AI can be applied to legal work, while 54% believe it should be applied to legal work, roughly the same rate as their law firm counterparts”.

Generative AI and LLMs could indeed be revolutionary in making the practice of law more efficient and cost effective, but this does not mean that they do not come with their own pitfalls.

Some of the known issues at this stage of the technology include:

AI Hallucinations

One of the most significant drawback in the Large Language Models is when the model generates inaccurate information but presents it as if it were true. This phenomena is known as AI hallucinations. These errors can be caused by a variety of factors, including insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model.

Biases

Generative AI models rely on the data they are trained on. The quality and diversity of the training data directly impacts the output of the model. If the training data is biased or limited in scope, the generated content may also reflect those biases or lack necessary diversity

Data Dependency

Further, another limitation is the requirement for massive amounts of good quality training data. Generative AI models usually require enormous datasets to learn from, in order to produce meaningful and accurate outputs. However, obtaining such datasets can be difficult and laborious, especially in areas where data collection is expensive or limited.

Contextual Understanding

Generative AI may find it difficult to understand context, especially when prompted with new information or situations outside of its training data. This can hamper its ability to derive conclusions or make decisions based on complex situations.

Conclusion

2023 was the year that Generative AI exploded and it became clear that it was here to stay. While the advent of any new technology poses its own risks, identifying areas that could benefit from the use of Generative AI and LLM tools and customising/ fine tuning these tools to tailor our needs could yield remarkable results in our legal practice. This technology at present or in the foreseeable future, might not be able to replace lawyers for the simple reason that it needs human oversight to work. These tools can do away with mundane and repetitive tasks leaving room for greater productivity. This conversation really just boils down to the catchphrase that is making rounds; “AI won’t replace lawyers, but lawyers who use AI will replace lawyers who don’t”. In conclusion the future of legal practice is a world where generative AI is an indispensable productivity tool, augmenting lawyers and their practice.