Standardized nomenclature for litigational legal prompting in generative language models - Aditya Sivakumar; Ben Gelman; Robert Simmons (Discover Artificial Intelligence, 2024)
Abstract With the increasing availability of commercial Artificial Intelligence, General Language Models (GLMs) have been widely explored in various domains, including law. However, to ensure accurate and standardized legal results, it is crucial to establish a consistent framework for prompting GLMs. This paper presents one of the first instances of such nomenclature, providing a robust framework of “variables” and “clauses” that enhances legal-focused results. The proposed framework was applied in diverse legal scenarios, demonstrating its potential from both client and attorney perspectives. By introducing standardized variables and clauses, legal professionals can effectively communicate with GLMs. This not only improves the accuracy of the generated outcomes but also facilitates collaboration between AI systems and legal experts. With a common framework in place, legal practitioners can leverage AI technology confidently, knowing that the results produced align with established legal principles. Furthermore, the framework serves as a foundation for future research in the field of legal prompting with GLMs, and several avenues for future research are recommended in this paper. This standardization of nomenclature is expected to contribute to the wider adoption and benefit of GLMs in the legal field, leading to more accurate and reliable outcomes.