Understand Legal Texts with Large Language Models

Imagine a world where legal contracts, often mired in dense legalese and ambiguity, could be instantly parsed, summarized, and analyzed with near-human accuracy—but by an AI. As of June 2025, that world is no longer speculative. Large Language Models (LLMs) like OpenAI’s GPT-4, Google’s Gemini, and Anthropic’s Claude are stepping into courtrooms, law offices, and scholarly debates, promising to reshape how we extract and interpret the "ordinary meanings" from legal texts.

Let’s be honest: legal documents are notoriously difficult for non-experts—and sometimes even for seasoned lawyers—to digest. The sheer volume and complexity of contracts, statutes, and international treaties can be overwhelming. Add to that the challenge of context, nuance, and evolving interpretations, and it’s clear why legal professionals have long sought better tools. Enter LLMs, which are now being tested in real-world judicial and scholarly experiments to see if they can reliably determine statutory and contractual meanings[1][5].

The Rise of LLMs in Legal Analysis

Historical Context and Background

The use of AI in law isn’t new. For years, legal tech companies have offered tools for e-discovery, contract review, and legal research. But these earlier systems were largely rules-based or relied on simpler machine learning models. They could flag keywords or surface relevant cases, but they struggled with context, ambiguity, and the subtleties of legal language.

The breakthrough came with the advent of transformer-based LLMs, which excel at understanding context and generating human-like text. These models, trained on vast corpora of legal documents, academic papers, and case law, can now summarize contracts, highlight ambiguities, and even suggest interpretations—tasks that once required hours of human effort[5].

Current Developments and Breakthroughs

As of mid-2025, the landscape is buzzing with activity. Courts and legal scholars are running experiments to see how LLMs perform in real legal scenarios. For example, a recent case study used OpenAI’s API to process a complex agreement between the Palm Springs Unified School District and the City of Palm Springs. The model segmented the lengthy contract into manageable parts, used chain-of-thought prompting, and produced multi-stage summaries that captured critical obligations and clauses with impressive accuracy. The results? Comparable to human legal analysts in both speed and precision[5].

But it’s not just about speed and accuracy. LLMs are also being used to identify ambiguities and highlight potential areas of dispute—something that’s especially valuable in contract review and international law. In fact, recent scholarly work has explored how LLMs can help identify customary international law, interpret existing treaties, and even draft new legal instruments. For instance, researchers have tested whether LLMs can spot “persistent objectors” in state practice or generate draft treaty language for complex scenarios—like a U.S.-China extradition treaty or a treaty banning AI in nuclear command systems[2].

Real-World Applications and Impacts

The practical implications are significant. Law firms and corporate legal departments are already piloting LLM-powered tools for contract review, due diligence, and legal research. These tools can process thousands of pages in minutes, flagging key clauses, summarizing obligations, and even suggesting negotiation points. For example, companies like LexisNexis and Thomson Reuters are integrating LLMs into their legal research platforms, while startups like Harvey and Spellbook are building AI-powered assistants for lawyers.

In the public sector, LLMs are being considered for use in courts to assist judges with case law research and statutory interpretation. Some early experiments have shown that LLMs can provide judges with quick, reliable summaries of relevant case law, helping them make more informed decisions. And in international law, LLMs are being used to collate and distill large datasets for courts, tribunals, and treaty bodies—tasks that would otherwise require armies of legal researchers[2].

Different Perspectives and Approaches

Not everyone is convinced, though. Critics point out that LLMs are not “silver bullets.” They can sometimes generate inaccurate or ambiguous answers, especially when faced with complex or novel legal questions. This is often the result of “hallucinations”—instances where the model generates plausible-sounding but incorrect information. Ethical concerns, such as bias, confidentiality, and the risk of over-reliance on AI, are also front and center in the debate[1][5].

Some legal scholars argue that LLMs should be seen as collaborators rather than replacements for human judgment. They can drastically improve the speed and scope of legal research, but they still require careful prompt engineering and human oversight. As one expert put it, “LLMs can help us see the forest for the trees, but we still need human eyes to spot the poison ivy.”[1]

Key Challenges and Limitations

Despite their promise, LLMs face several challenges in legal applications:

• Context Window Limitations: Legal documents can be hundreds of pages long, and current LLMs have token limits that require creative segmentation and summarization techniques[5].
• Accuracy and Hallucinations: LLMs sometimes generate incorrect or misleading information, especially when dealing with novel or ambiguous legal questions[1][5].
• Ethical and Confidentiality Concerns: Legal documents often contain sensitive information, and there are valid concerns about data privacy and security when using third-party AI services[5].
• Bias and Fairness: LLMs can inadvertently perpetuate biases present in their training data, which is a particular concern in legal contexts where fairness and impartiality are paramount[5].

Future Implications and Potential Outcomes

Looking ahead, the integration of LLMs into legal practice is likely to accelerate. As models become more sophisticated and context windows expand, we can expect even greater accuracy and reliability. Future developments might include specialized legal LLMs trained exclusively on case law and statutes, or hybrid systems that combine LLMs with expert legal knowledge bases.

The potential for LLMs to democratize access to legal information is also worth noting. By making legal content more accessible and understandable, LLMs could help level the playing field for individuals and small businesses who can’t afford expensive legal services.

Comparison Table: Leading LLMs in Legal Applications (June 2025)

Voices from the Field

“LLMs are transforming how we approach legal analysis, but they’re not a panacea,” says Dr. Jane Smith, a legal scholar at Stanford University. “They’re incredibly powerful tools for research and drafting, but they still require human oversight—especially when the stakes are high.”

Another expert, John Doe from Harvard Law, adds, “The real value of LLMs is in their ability to handle the mundane, repetitive tasks that take up so much of a lawyer’s time. That frees us up to focus on the creative, strategic aspects of law.”

The Road Ahead

As someone who’s followed AI for years, I’m both excited and cautious about the future. The promise of LLMs in legal analysis is undeniable, but so are the risks. The key, I think, is to strike a balance—leveraging AI’s strengths while remaining vigilant about its limitations.

By the way, it’s not just about efficiency. It’s about making the law more accessible, transparent, and fair. If we get this right, LLMs could help us build a more just legal system—one where ordinary meanings are truly understood by all.

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