Generative AI in Legal Dispute Resolution: Opportunities and Challenges

The rapid rise of Generative AI, including large language models (LLMs), offers tremendous possibilities for enhancing legal workflows, yet law firms face unique challenges in adopting these tools. This article provides a description of how LLMs work, lists their inherent deficiencies, and offers tentative conclusions on their suitability for use in commercial dispute resolution.

How Gen AI Models Work and How They Differ from Traditional AI

AI technologies have powered automation in software tools used by lawyers for decades. Documents are scanned with OCR technology, spell and grammar checkers use NLP, and automated translation software uses NMT. Although we rely on these technologies, we recognize they are limited and do not provide accuracy for all applications.

AI technologies have become more sophisticated, moving from simple rule-based algorithms to machine learning models that identify patterns directly from data and make predictions. Complex AI models like those used for weather prediction are probabilistic, assigning weights to variables and making predictions based on diverse data inputs.

LLMs function similarly. They predict plausible sequences of words based on a given input, with each word assigned a probability score based on context. Because LLMs generate new outputs, rather than limit themselves to evaluating or classifying input data, they are referred to as Generative AI.

LLMs map words semantically, allowing them to understand text essence and perform tasks like summarizing or translating. However, they lack reasoning capability and generate responses based on statistical patterns learned during training. There is also a common misconception that LLMs remember every user query, which is incorrect; they operate in two distinct phases: training and operation. During the operation phase, they generate text based on patterns learned in training and any context provided by the user.

Limitations of LLMs and How They Can Be Mitigated

LLMs are versatile but suffer from various deficiencies:

1. They excel at general knowledge but underperform in specialized areas.
2. They don’t know information post-dating their training.
3. They lack user-specific preferences unless specified in the query.
4. They are suggestible and may reflect biases.
5. Outputs vary significantly with minor changes in input.
6. They replicate biases in their training data.
7. They struggle with accurate citations and may “hallucinate.”
8. They struggle with complex tasks requiring step-by-step reasoning.
9. Their operations are a “black box,” without human-intelligible logic.
10. They are more costly and slower than simpler AI models.

Techniques to mitigate these issues include:

• Prompt Engineering: Enhancing queries with detailed instructions and examples.
• Auxiliary Tools: Augmenting LLMs with tools like RAG for semantic retrieval, reducing hallucinations and enabling citation.
• Finetuning: Retraining models with domain-specific data to improve accuracy.

These methods help optimize LLM performance without changing the underlying model.

Evaluating LLMs for Dispute Resolution Use Cases

Lawyers and dispute resolution professionals spend much time with written text, so it is natural to ask how LLMs can assist. Many of their limitations can be mitigated through prompt engineering, auxiliary tools, and finetuning, though some limitations remain.

Effective LLM use cases include document summarization, translation, reformatting, brainstorming, and answering questions based on provided sources. When paired with semantic retrieval tools, LLMs are also effective in mining databases for relevant information. However, certain tasks, like complex legal analysis, decision-making, and evaluating evidence-based arguments, are still best handled by rule-based algorithms for their transparency and logical consistency.

In the future, AI may combine the strengths of multiple AI technologies to unlock new use cases. LLMs, for instance, can extract details from documents, which can then feed into traditional rule-based systems. However, it’s essential to understand how each AI application works to ensure quality and mitigate risks. With careful use, AI tools can enhance dispute resolution professionals’ work without replacing human judgment.