BLAST: Benchmarking LLMs with ASP-based Structured Testing
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 ...
| Authors | Manuel Alejandro Borroto Santana, Erica Coppolillo, Francesco Calimeri et al. |
|---|---|
| Primary category | cs.LO |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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