Large Language Models (LLMs) have demonstrated remarkable performance across a broad spectrum of tasks, including natural language understanding, dialogue systems, and code generation. Despite evident progress, less attention has been paid to their effectiveness in handling declarative paradigms such as Answer Set Programming (ASP), to date. In this paper we introduce BLAST: The first dedicated benchmarking methodology and associated dataset for evaluating the accuracy of LLMs in generating ASP code. BLAST provides a structured evaluation framework featuring two novel semantic metrics tailored to ASP code generation. The paper presents the results of an empirical evaluation involving ten well-established graph-related problems from the ASP literature and a diverse set of eight state-of-the-art LLMs.
@article{arxiv.2604.22306,
title = {BLAST: Benchmarking LLMs with ASP-based Structured Testing},
author = {Manuel Alejandro Borroto Santana and Erica Coppolillo and Francesco Calimeri and Giuseppe Manco and Simona Perri and Francesco Ricca},
journal= {arXiv preprint arXiv:2604.22306},
year = {2026}
}