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[2504.21022] ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees
Summary
The paper presents ConformalNL2LTL, a novel method for translating natural language instructions into Linear Temporal Logic (LTL) formulas, ensuring correctness through conformal prediction.
Why It Matters
This research addresses the challenge of accurately translating natural language into formal specifications for autonomous systems, which is crucial for enhancing the reliability and usability of AI applications. By providing correctness guarantees, it reduces the manual effort required in task specification, making it easier for users to interact with complex systems.
Key Takeaways
- ConformalNL2LTL achieves user-defined translation success rates for unseen commands.
- The method uses a collaborative approach with primary and auxiliary models to ensure accuracy.
- Conformal prediction quantifies uncertainty and minimizes user intervention in the translation process.
- This approach enhances the usability of LTL in autonomous systems by simplifying task specification.
- The research demonstrates both theoretical and empirical validation of the method's effectiveness.
Computer Science > Computation and Language arXiv:2504.21022 (cs) [Submitted on 22 Apr 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees Authors:David Smith Sundarsingh, Jun Wang, Jyotirmoy V. Deshmukh, Yiannis Kantaros View a PDF of the paper titled ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees, by David Smith Sundarsingh and 3 other authors View PDF HTML (experimental) Abstract:Linear Temporal Logic (LTL) is a widely used task specification language for autonomous systems. To mitigate the significant manual effort and expertise required to define LTL-encoded tasks, several methods have been proposed for translating Natural Language (NL) instructions into LTL formulas, which, however, lack correctness guarantees. To address this, we propose a new NL-to-LTL translation method, called ConformalNL2LTL that achieves user-defined translation success rates on unseen NL commands. Our method constructs LTL formulas iteratively by solving a sequence of open-vocabulary question-answering (QA) problems using large language models (LLMs). These QA tasks are handled collaboratively by a primary and an auxiliary model. The primary model answers each QA instance while quantifying uncertainty via conformal prediction; when it is insufficiently certain according to user-defi...