ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees
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Abstract (arXiv)
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-defined confidence thresholds, it requests assistance from the auxiliary model and, if necessary, from the user. We demonstrate theoretically and empirically that ConformalNL2LTL achieves the desired translation accuracy while minimizing user intervention.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 50 of its 79 references that are in the map. First posted on arXiv 22 Apr 2025.
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50 of 79 references.
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Built on by
Citing papers per year: 2025: 3; 2026: 8.
11 papers.
- 2025Verifiable Natural Language to Linear Temporal Logic Translation: A Benchmark Dataset and Evaluation Suitecited by 4
- 2026From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agentscited by 3
- 2026Fixing FOLIO and MALLS: Verified Annotations and an LLM-assisted Framework to Focus Human Relabelingcited by 1
- 2026Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulationcited by 1
- 2026Towards General Language-Conditioned Latent Safety Filterscited by 1
- 2026SCP-NL2TL: Selective Conformal Prediction with Semantic Verification for Natural Language to Temporal Logic Specificationscited by 1
- 2025Ask, Reason, Assist: Robot Collaboration via Natural Language and Temporal Logiccited by 0
- 2025CoFineLLM: Conformal Finetuning of LLMs for Language-Instructed Robot Planningcited by 0
- 2026Specification-Guided Synthesis of Deadlock-Free Communication Protocol Refinements with Large Language Modelscited by 0
- 2026Linear Temporal Logic Translation via Human-Inspired Self-Constrained Reasoning for Robot Task Specificationcited by 0
- 2026MENTOR: Procedural Shielding of Tool-Using LLM Agentscited by 0
- 2026MENTOR: Procedural Shielding of Tool-Using LLM Agents12 Oct 2026
- 2026Linear Temporal Logic Translation via Human-Inspired Self-Constrained Reasoning for Robot Task Specification28 Aug 2026
- 2026SCP-NL2TL: Selective Conformal Prediction with Semantic Verification for Natural Language to Temporal Logic Specifications5 Aug 2026
- 2026Towards General Language-Conditioned Latent Safety Filters31 Jul 2026
- 2026Specification-Guided Synthesis of Deadlock-Free Communication Protocol Refinements with Large Language Models30 Jul 2026
- 2026Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulation4 Jun 2026
- 2026Fixing FOLIO and MALLS: Verified Annotations and an LLM-assisted Framework to Focus Human Relabeling1 Jun 2026
- 2026From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents27 Mar 2026
- 2025CoFineLLM: Conformal Finetuning of LLMs for Language-Instructed Robot Planning9 Nov 2025
- 2025Ask, Reason, Assist: Robot Collaboration via Natural Language and Temporal Logic27 Sep 2025
- 2025Verifiable Natural Language to Linear Temporal Logic Translation: A Benchmark Dataset and Evaluation Suite1 Jul 2025