nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models

TL;DR

nl2spec leverages large language models to convert natural language system requirements into formal temporal logic specifications, enabling interactive refinement and reducing manual effort in formal verification tasks.

Contribution

This work introduces a novel LLM-based framework for translating unstructured natural language into formal temporal logic specifications with an interactive ambiguity resolution process.

Findings

Effective translation of natural language to temporal logic demonstrated.

User study shows improved accuracy with interactive refinement.

Open-source tool available for community use.

Abstract

A rigorous formalization of desired system requirements is indispensable when performing any verification task. This often limits the application of verification techniques, as writing formal specifications is an error-prone and time-consuming manual task. To facilitate this, we present nl2spec, a framework for applying Large Language Models (LLMs) to derive formal specifications (in temporal logics) from unstructured natural language. In particular, we introduce a new methodology to detect and resolve the inherent ambiguity of system requirements in natural language: we utilize LLMs to map subformulas of the formalization back to the corresponding natural language fragments of the input. Users iteratively add, delete, and edit these sub-translations to amend erroneous formalizations, which is easier than manually redrafting the entire formalization. The framework is agnostic to specific application domains and can be extended to similar specification languages and new neural models. We perform a user study to obtain a challenging dataset, which we use to run experiments on the quality of translations. We provide an open-source implementation, including a web-based frontend.