NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models (2023.emnlp-main)
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| Challenge: | Temporal Logic (TL) can be used to specify complex high-level specifications for systems in many engineering domains. |
| Approach: | They propose a framework for translation between NL and TL using Large Language Models . they use a dataset to create a model with 23K NL-TL pairs and human annotation . |
| Outcome: | The proposed framework achieves higher accuracy (> 95%) using only 10% training data compared with baseline model. |
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| Challenge: | Temporal Logic (STL) is a formal specification tool for cyber-physical systems . but it is difficult to transform ambiguous and complex data into STL, a paper argues . |
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NL2Logic: AST-Guided Translation of Natural Language into First-Order Logic with Large Language Models (2026.findings-eacl)
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| Challenge: | Structured reasoning approaches that parse first-order logic rules from natural language lack syntax control and semantic faithfulness. |
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Large Language Models Can Learn Temporal Reasoning (2024.acl-long)
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| Challenge: | Temporal reasoning (TR) is a fundamental ability of large language models (LLMs) however, there is neo-standard methods to perform TR, which are not suitable for large language model applications. |
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Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation (2024.acl-long)
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| Challenge: | Existing methods to improve neural language models perform poorly on emerging data. |
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Temporal Reasoning in Natural Language Inference (2020.findings-emnlp)
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| Challenge: | We use five new natural language inference (NLI) datasets focused on temporal reasoning. |
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Neural Language Modeling for Contextualized Temporal Graph Generation (2021.naacl-main)
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| Challenge: | Existing methods for temporal reasoning have been used for a number of applications, but their potential for tempor reasoning over event graphs has not been explored. |
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Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data (2026.eacl-long)
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| Challenge: | Large language models are being rapidly applied across many fields such as healthcare, finance, transportation, and energy. |
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FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)
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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev
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Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Temporal reasoning is a vital component of human communication and understanding, yet remains an underexplored area within the context of Large Language Models (LLMs). |
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