The authors create a novel dataset and show that fine-tuned small open-weight LLMs can translate natural language to ATL/ATL* specifications with semantic accuracy (0.84) statistically matching strong few-shot proprietary baselines (0.86).
The Monist29(1), 32–63 (1919)
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Translating Natural Language to Strategic Temporal Specifications via LLMs
The authors create a novel dataset and show that fine-tuned small open-weight LLMs can translate natural language to ATL/ATL* specifications with semantic accuracy (0.84) statistically matching strong few-shot proprietary baselines (0.86).