The law, an intrinsically language-driven profession, has for centuries thrived on the precision of its jargon to ensure clarity and consistency. As we look to the future, advances in Large Language Models (LLMs), Generative AI (GenAI), and the broader AI domain hint at profound transformations in the legal landscape, particularly for non-lawyers.
1. Understanding LLMs, GenAI, and AGI
LLMs, represented by models like GPT, LLaMa, and Alpaca, are rooted in the realm of artificial intelligence. More specifically, they're a subset of Deep Learning models known as Transformer models. These models, distinguished by their vast parameters, are adept at predicting and generating sequences of words. And for this, they are called “large”.
You can think of previous models as a librarian directing you to the right section of the library, to the right shelf, and maybe they can recommend a few titles. An LLM instead is like a master librarian who can recall details from every page of every book in that library and can give you explanations tailored to your questions and deeply informed perspectives, based on all they have "read" and “understood”.
Historically, text analysis heavily relied on Recurrent Neural Networks (RNN). However, since the advent of Transformers in 2017, text analysis has been revolutionized, offering significantly faster processing.
GenAI, an application of LLMs, is dedicated to generating new data by mirroring existing datasets. In the legal domain, this translates to tasks like contract drafting, patent application creation, and offering basic legal advice through chatbots. Importantly, GenAI should not be confused with artificial general intelligence (AGI) - a form of AI that can replicate any intellectual human activity.
2. Legaltech's Evolution: Empowering Non-Lawyers
Legal language is foundational to contracts and legal proceedings. Its intricate vocabulary serves as the bedrock of clarity and protection in legal dealings. However, LLMs and GenAI are challenging this status quo. These advancements aren't just redefining how we use and comprehend legal language; they are democratizing access to legal information and tools.
Technologies like text classification and named entity recognition (NER) have already made waves in legal tech, facilitating automated processing and categorization of vast amounts of legal texts. Legaltech, a rapidly expanding field, spans a multitude of tools:
- Contract Lifecycle Management platforms: These handle the entire life cycle of a contract, from its inception to its expiration or termination.
- Contract review and analytics platforms: These allow for quick analysis of contracts, highlighting potential issues, suggested fixes, and areas for negotiation.
- Ediscovery software: Tools that assist in finding, collecting, and producing electronically stored information (ESI) for evidence in lawsuits.
- Practice management software: Comprehensive solutions for managing client information, cases, billing, and more.
While many of these tools were initially crafted exclusively for the domain of legal professionals, the rise of LLMs and GenAI is making law more accessible and user-friendly for the general public. The line between professional legal tools and those for the everyday person is becoming increasingly blurred, marking a new chapter in the realm of legal assistance.
3. Challenges Ahead for Legal Teams
Data Management and Compliance
With the increasing amount of data being generated and stored by companies, managing, sorting, and analyzing this data, ensuring its security, and making it available for e-discovery in litigation has become a challenge.
Data Privacy, Regulation and Compliance
With the rapid growth of technology, safeguarding sensitive information and staying compliant with evolving privacy regulations is key.
There has been an increase in regulations around privacy (like GDPR in Europe and CCPA in California), cybersecurity, and other tech-related issues. Legal teams must stay updated with these regulations, understand their implications, and ensure their organizations remain compliant.
Technological Evolution and Integration of Tech
The rise of AI, blockchain, and other technologies poses opportunities and challenges. Legal teams must understand the implications of these technologies, especially in areas like intellectual property, contract law, and liability issues.
In addition, seamless integration of new technologies into traditional legal workflows can be challenging.
Ethical Concerns with AI and Technology
As legal teams start adopting AI for tasks like contract review or prediction of litigation outcomes, there are ethical concerns about the transparency, fairness, and accountability of these AI systems.
4. LLM Integration in the Legal Sector: Points to Ponder
The integration of LLMs in the legal domain is a promising venture, but it is riddled with complexities:
Hallucination
One major concern with LLMs is "hallucination", where the model might generate false or misleading information. In a legal setting, this can have dire consequences, from misinterpretation of laws to incorrect legal advice.
Data Limitations
As AI models become increasingly sophisticated, acquiring quality legal texts to train them becomes a challenge. Legal documents are often sensitive, confidential, and not easily accessible, making it difficult to source them in large quantities.
Quality Data Imperative
Using accurate, up-to-date, and unbiased legal data sources is essential. Relying on outdated or erroneous legal texts could propagate outdated or incorrect legal principles.
In light of these challenges, many experts propose measures to enhance the reliability of LLMs. One such solution is the "Rewrite and Rollback" (R&R) framework. This method allows for constant data revisions, ensuring the models remain accurate. By employing R&R, not only can the accuracy of the model be maintained, but the overall quality of generated texts can be improved, benefiting human writers and data creation alike.
5. What will the next 12 - 48 months look like?
In the next 12 months, we will see a focus on several new applications on top of foundational models (LLMs), such as AI contract drafting tools and AI contract review assistants.
Forty-eight months from now, we will potentially see AI agents negotiating contracts for us, and we may sit back and watch them do the work for us. Sounds a bit like SciFi? It sounds so, but it may become a reality in the next few years.
Imagine typing your AI legal assistant the terms you're willing to accept, as well as the deal-breakers that might make you walk away from negotiations. Your AI agent will use these criteria to strategize effectively, pitted against another party's agent who's doing likewise. The eventual agreement might strike a balance based on the input from both sides.
I believe that in the upcoming 12 months, human input will dominate over machines. However, in the longer term (over 48 months), the human input will diminish and change as technology advances. This "smarter" technology will be able to process a broader range of data points, including nuanced aspects like an individual's character and personal preferences, all at once.
I expect the dominant companies of tomorrow to rethink every industry with intelligence.
The dominant consumer company of today (TikTok) rethought the content experience with personalized AI.
We believe better tooling, cheaper and more abundant computing, more data, more open-source models, and more AI-educated talent will lead to ten thousand experiments.
If Software 1.0 is about human-written code, and Software 2.0 is about dataset labeling, Software 3.0 will be about manipulating foundation models like LLMs.
If we conceptualize past software generations as being about human code and dataset labeling, the next wave might be about refining foundational models, i.e. LLMs.
Therefore, the legal profession will likely undergo a transformation, blending traditional legal expertise with technological prowess. Legaltech tools and LLMs may soon become first-line legal consultants, parsing vast amounts of data to provide preliminary advice, which human lawyers will then refine and finalize. Ethical considerations will also come to the forefront, with discussions on AI's role in decision-making and ensuring unbiased, just outcomes.
6. Conclusion
In conclusion, the amalgamation of LLMs and GenAI in law promises greater efficiency and precision. Nevertheless, the road ahead necessitates a delicate balance between the might of technology and indispensable human discernment. The anticipated rise of LLMs in various legal domains underscores the promise of enhanced efficiency. Yet, the innate sensitivities of legal endeavours will demand balancing the power of technology with critical human judgment.
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