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

Cosler, Matthias and Hahn, Christopher and Mendoza, Daniel and Schmitt, Frederik and Trippel, Caroline
(2023) nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models.
In: 35th International Conference on Computer Aided Verification (CAV 2023).
Conference: CAV Computer Aided Verification

[img] Text
nl2spec - Springer.pdf

Download (655kB)

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.

Actions

Actions (login required)

View Item View Item