Towards the LLM-Based Generation of Formal Specifications from Natural-Language Contracts: Early Experiments with Symboleo
Mounira Nihad Zitouni, Amal Ahmed Anda, Sahil Rajpal, Daniel Amyot, John Mylopoulos
TL;DR
Early results on all LLMs show promising outcomes (even for a little-known DSL) that will likely accelerate the specification of legal contracts, but several observed issues, especially around grammar/syntax adherence and environment variable identification, suggest many areas where potential improvements should be investigated.
Abstract
Over the past decade, different domain-specific languages (DSLs) were proposed to formally specify requirements stated in legal contracts, mainly for analysis but also for code generation. Symboleo is a promising language in that area. However, writing formal specifications from natural-language contracts is a complex task, especial for legal experts who do not have formal language expertise. This paper reports on an exploratory experiment targeting the automated generation of Symboleo specifications from business contracts in English using Large Language Models (LLMs). Combinations (38) of prompt components are investigated (with/without the grammar, semantics explanations, 0 to 3 examples, and emotional prompts), mainly on GPT-4o but also to a lesser extent on 4 other LLMs. The generated specifications are manually assessed against 16 error types grouped into 3 severity levels. Early results on all LLMs show promising outcomes (even for a little-known DSL) that will likely accelerate the specification of legal contracts. However, several observed issues, especially around grammar/syntax adherence and environment variable identification (49%), suggest many areas where potential improvements should be investigated.