GINSIGN: GROUNDING NATURAL LANGUAGE INTO SYSTEM SIGNATURES FOR TEMPORAL LOGIC TRANSLATION
Unlocking Precise AI for Temporal Logic Translation
Bridging the gap between natural language and actionable system specifications with GinSign.
Executive Impact Summary
GinSign dramatically improves the accuracy of Natural Language to Temporal Logic (NL-to-TL) translation by introducing a novel hierarchical grounding framework. This enables precise mapping of NL specifications to system signatures, leading to verifiable and deployable AI systems.
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Grounded Logical Equivalence
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Improvement over SOTA
Deep Analysis & Enterprise Applications
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Hierarchical Grounding Framework
Leveraging System Signatures
Scalable BERT-based Grounding
Hierarchical Grounding Framework
GinSign decomposes the complex grounding task into two distinct, manageable steps: predicate grounding and argument grounding. This hierarchical approach simplifies the classification problem, making it tractable for smaller models and eliminating reliance on expensive Large Language Models (LLMs).
By first classifying the predicate and then its appropriately typed arguments, GinSign ensures a logically coherent and semantically sound translation.
Leveraging System Signatures
The framework integrates system signatures (T, P, C) – formal vocabularies defining types, predicates, and constants – to provide explicit semantic definitions for atomic propositions. This grounding in a system's formal structure is crucial for making TL formulas operationally meaningful and verifiable.
It allows the AI to understand the 'world' it operates in, translating abstract NL into concrete, executable logic.
Scalable BERT-based Grounding
Instead of fixed soft-max heads, GinSign uses a BERT encoder that learns to score span-prefix alignments, making the model domain-agnostic and transferable. The use of a 'prefix sharding' and 'tournament reduction' strategy ensures scalability to arbitrarily large signatures, outperforming LLMs by a factor of 1.4x.
This efficiency gain is critical for real-world deployment where system vocabularies can be extensive.
95.5%
Grounded Logical Equivalence Achieved by GinSign
This metric signifies the ability to produce not only syntactically correct LTL but also to correctly ground all atomic propositions within the system's signature, allowing for actual model checking and verification.
Enterprise Process Flow
Natural Language Input
→
Lifting (NL to Lifted APs)
→
Predicate Grounding
→
Argument Grounding
→
Lifted LTL Translation
→
Grounded LTL Output
| Framework | Lifting | Translation | Grounding |
|---|---|---|---|
| LLM-Baseline | |||
| NL2LTL (Fuggitti & Chakraborti, 2023) | |||
| NL2TL (Chen et al., 2023) | |||
| Lang2LTL (Liu et al., 2023) | |||
| GinSign (ours) |
Real-world Impact: Autonomous Systems
Consider the application of GinSign in autonomous robot navigation. A command like 'The robot must find the bookbag and then deliver it to shipping' needs precise translation.
Traditional methods might generate a syntactically correct but semantically ambiguous LTL. GinSign, however, leverages a system signature defining `search(Item)` and `deliver(Item, Location)` with `backpack: Item` and `loading_dock: Location` as constants.
This allows it to output `◇(search(backpack) ^ ◇(deliver(backpack, loading_dock)))`, which is fully grounded and verifiable against a robot's state machine, preventing costly errors and ensuring reliable autonomous operation.
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Your GinSign Implementation Roadmap
A phased approach to integrate GinSign into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Signature Mapping
Initial workshop to understand existing NL specification processes and map core system predicates, types, and constants into a formal GinSign system signature.
Phase 2: Model Training & Fine-tuning
Leverage your historical NL specifications to fine-tune GinSign's grounding and translation models, ensuring optimal performance for your specific domain.
Phase 3: Integration & Validation
Integrate GinSign with your existing verification tools and perform rigorous validation using real-world traces to confirm grounded logical equivalence.
Phase 4: Pilot Deployment & Iteration
Begin a pilot program with a small team, gather feedback, and iterate on the system signature and model configurations for continuous improvement and broader rollout.
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