Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge

• URL: http://arxiv.org/abs/2505.20658v2
• Date: Thu, 24 Jul 2025 09:02:40 GMT
• Title: Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge
• Authors: Yue Fang, Zhi Jin, Jie An, Hongshen Chen, Xiaohong Chen, Naijun Zhan,
• Abstract summary: We propose an NL-STL dataset named STL-Diversity-Enhanced (STL-DivEn), which comprises 16,000 samples enriched with diverse patterns.<n>To develop the dataset, we first manually create a small-scale seed set of NL-STL pairs.<n> representative examples are identified through clustering and used to guide large language models.<n>Finally, diversity and accuracy are ensured through rigorous rule-based filters and human validation.
• Score: 23.50725254650578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal Logic (TL), especially Signal Temporal Logic (STL), enables precise formal specification, making it widely used in cyber-physical systems such as autonomous driving and robotics. Automatically transforming NL into STL is an attractive approach to overcome the limitations of manual transformation, which is time-consuming and error-prone. However, due to the lack of datasets, automatic transformation currently faces significant challenges and has not been fully explored. In this paper, we propose an NL-STL dataset named STL-Diversity-Enhanced (STL-DivEn), which comprises 16,000 samples enriched with diverse patterns. To develop the dataset, we first manually create a small-scale seed set of NL-STL pairs. Next, representative examples are identified through clustering and used to guide large language models (LLMs) in generating additional NL-STL pairs. Finally, diversity and accuracy are ensured through rigorous rule-based filters and human validation. Furthermore, we introduce the Knowledge-Guided STL Transformation (KGST) framework, a novel approach for transforming natural language into STL, involving a generate-then-refine process based on external knowledge. Statistical analysis shows that the STL-DivEn dataset exhibits more diversity than the existing NL-STL dataset. Moreover, both metric-based and human evaluations indicate that our KGST approach outperforms baseline models in transformation accuracy on STL-DivEn and DeepSTL datasets.

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