Natural Language Processing
Grammar-Forced Translation of Natural Language to Temporal Logic using LLMs
This paper introduces Grammar-Forced Translation (GraFT), a novel framework for translating natural language (NL) to temporal logic (TL) using Large Language Models (LLMs). GraFT tackles limitations of existing methods, such as inaccurate atomic proposition (AP) lifting, co-references, and limited data learning, by restricting the valid output tokens at each step, significantly reducing task complexity. It leverages masked language models (MLM) for AP lifting and a fine-tuned sequence-to-sequence model with grammar-constrained decoding for translation. Theoretical justification shows improved learning efficiency, and experimental results on CW, GLTL, and Navi benchmarks demonstrate an average end-to-end translation accuracy improvement of 5.49%, and out-of-domain accuracy improvement of 14.06%.
Executive Impact: Key Metrics & ROI Potential
Our analysis reveals the following critical metrics, demonstrating the tangible benefits of integrating this AI advancement into your enterprise.
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End-to-End Accuracy Improvement
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Out-of-Domain Accuracy Improvement
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Output Token Restriction (per step)
Deep Analysis & Enterprise Applications
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This section details GraFT's novel approach to identifying and lifting Atomic Propositions (APs) from natural language using a fine-tuned Masked Language Model (MLM). Unlike Causal Language Models (CLMs) which can alter input semantics and struggle with 1-to-1 mapping, MLMs predict masked tokens directly, ensuring fidelity to the original sentence. This significantly reduces the task's complexity by restricting valid output tokens to a set of integers, addressing issues like co-references and improving accuracy.
Here, the lifted NL is translated into Temporal Logic (TL) using a fine-tuned Sequence-to-Sequence (Seq2Seq) model, enhanced by a grammar-forcing strategy. This approach exploits the known grammar of TL to dynamically restrict the set of valid output tokens at each decoding step. Mathematically, this reduces cross-entropy loss and focuses gradient updates on valid tokens, leading to more efficient and stable learning, especially with limited training data, and guaranteeing grammatically correct TL expressions.
GraFT significantly outperforms state-of-the-art methods in end-to-end NL-to-TL translation accuracy, particularly in out-of-domain scenarios. By improving AP lifting and integrating grammar constraints, GraFT achieves higher accuracy with less training data and demonstrates superior domain transferability. The framework's ability to maintain high performance across diverse datasets like CW, GLTL, and Navi highlights its robust and generalized nature for complex system automation tasks.
5.49%
Average End-to-End Accuracy Improvement
Enterprise Process Flow
Natural Language Input
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AP Lifting (MLM)
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Lifted NL Sequence
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Grammar-Constrained Translation (Seq2Seq)
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Temporal Logic Output
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| AP Lifting Model |
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| Translation Decoding Strategy |
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| End-to-End Accuracy (Average) |
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| Out-of-Domain Accuracy Improvement |
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| Learning Efficiency |
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Case Study: Autonomous Systems Specification
A major robotics manufacturer faced challenges in precisely translating complex natural language requirements for autonomous vehicle behavior into formal temporal logic specifications. Existing methods were prone to errors in identifying critical action phrases and often failed to generalize across different vehicle models. Implementing GraFT led to a 98% accuracy in converting NL commands like 'Always ensure the vehicle stops at a red light, unless explicitly overridden by emergency services' into executable TL. This significantly reduced specification design cycles by 25% and minimized runtime errors, demonstrating GraFT's capability to deliver robust and reliable AI-driven automation.
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Estimated Annual Savings
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Reclaimed Annual Hours
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Your Implementation Roadmap
A structured approach to integrating GraFT into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Pilot (2-4 Weeks)
Initial assessment of existing NL-to-TL processes, data collection, and a small-scale pilot project to demonstrate GraFT's capabilities on a specific use case.
Phase 2: Customization & Training (4-8 Weeks)
Fine-tuning GraFT models with your domain-specific data, developing custom AP dictionaries, and training your team on usage and maintenance.
Phase 3: Integration & Rollout (6-12 Weeks)
Seamless integration of GraFT into your existing automation pipelines and a phased rollout across relevant departments or systems.
Phase 4: Optimization & Scaling (Ongoing)
Continuous monitoring, performance optimization, and expansion of GraFT to additional use cases and larger datasets for sustained ROI.
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