NL2LOGIC: AST-Guided Translation of Natural Language into First-Order Logic with Large Language Models
arXiv:2602.13237v1 Announce Type: new Abstract: Automated reasoning is critical in domains such as law and governance, where verifying claims against facts in documents requires both accuracy and interpretability. Recent work adopts structured reasoning pipelines that translate natural language into first-order logic and delegate inference to automated solvers. With the rise of large language models, approaches such as GCD and CODE4LOGIC leverage their reasoning and code generation capabilities to improve logic parsing. However, these methods suffer from fragile syntax control due to weak enforcement of global grammar constraints and low semantic faithfulness caused by insufficient clause-level semantic understanding. We propose NL2LOGIC, a first-order logic translation framework that introduces an abstract syntax tree as an intermediate representation. NL2LOGIC combines a recursive large language model based semantic parser with an abstract syntax tree guided generator that determini
arXiv:2602.13237v1 Announce Type: new Abstract: Automated reasoning is critical in domains such as law and governance, where verifying claims against facts in documents requires both accuracy and interpretability. Recent work adopts structured reasoning pipelines that translate natural language into first-order logic and delegate inference to automated solvers. With the rise of large language models, approaches such as GCD and CODE4LOGIC leverage their reasoning and code generation capabilities to improve logic parsing. However, these methods suffer from fragile syntax control due to weak enforcement of global grammar constraints and low semantic faithfulness caused by insufficient clause-level semantic understanding. We propose NL2LOGIC, a first-order logic translation framework that introduces an abstract syntax tree as an intermediate representation. NL2LOGIC combines a recursive large language model based semantic parser with an abstract syntax tree guided generator that deterministically produces solver-ready logic code. Experiments on the FOLIO, LogicNLI, and ProofWriter benchmarks show that NL2LOGIC achieves 99 percent syntactic accuracy and improves semantic correctness by up to 30 percent over state-of-the-art baselines. Furthermore, integrating NL2LOGIC into Logic-LM yields near-perfect executability and improves downstream reasoning accuracy by 31 percent compared to Logic-LM's original few-shot unconstrained translation module.
Executive Summary
The article 'NL2LOGIC: AST-Guided Translation of Natural Language into First-Order Logic with Large Language Models' introduces a novel framework for translating natural language into first-order logic, leveraging large language models and abstract syntax trees. The framework, NL2LOGIC, aims to address the challenges of syntax control and semantic faithfulness in automated reasoning tasks, particularly in domains like law and governance. The authors demonstrate significant improvements in syntactic accuracy and semantic correctness over existing methods, as evidenced by experiments on multiple benchmarks. Integration with Logic-LM further enhances reasoning accuracy and executability.
Key Points
- ▸ NL2LOGIC introduces an abstract syntax tree (AST) as an intermediate representation for translating natural language into first-order logic.
- ▸ The framework combines a recursive semantic parser with an AST-guided generator to improve syntax control and semantic understanding.
- ▸ Experiments show NL2LOGIC achieves 99% syntactic accuracy and up to 30% improvement in semantic correctness over state-of-the-art baselines.
- ▸ Integration with Logic-LM improves downstream reasoning accuracy by 31% compared to unconstrained translation modules.
Merits
Improved Syntactic Accuracy
NL2LOGIC achieves near-perfect syntactic accuracy, ensuring that the generated logic code is solver-ready and reducing the likelihood of syntax-related errors.
Enhanced Semantic Faithfulness
The framework's use of an AST and recursive semantic parsing significantly improves semantic understanding, leading to more accurate translations of natural language into logic.
Integration with Existing Systems
NL2LOGIC's compatibility with Logic-LM and other reasoning systems enhances its practical applicability and demonstrates its potential to improve existing automated reasoning pipelines.
Demerits
Complexity of Implementation
The integration of AST and recursive parsing mechanisms may increase the complexity of the system, potentially requiring more computational resources and expertise to implement effectively.
Limited Benchmark Diversity
While the experiments cover multiple benchmarks, the results may not be generalizable to all domains or types of natural language reasoning tasks.
Dependency on Large Language Models
The performance of NL2LOGIC is contingent on the capabilities of the underlying large language models, which may have limitations in understanding context or handling ambiguous language.
Expert Commentary
The article presents a significant advancement in the field of automated reasoning by addressing critical challenges in syntax control and semantic faithfulness. The introduction of an abstract syntax tree as an intermediate representation is a novel approach that effectively leverages the capabilities of large language models. The experimental results demonstrate substantial improvements over existing methods, particularly in terms of syntactic accuracy and semantic correctness. However, the complexity of implementing such a system and its dependency on large language models are notable limitations. The practical implications of this research are profound, especially in domains like law and governance, where accurate and interpretable reasoning is paramount. The integration of NL2LOGIC with Logic-LM further underscores its potential to enhance existing reasoning pipelines. From a policy perspective, the advancements in automated reasoning may influence how legal and regulatory frameworks are analyzed and interpreted, raising important questions about the transparency and accountability of AI systems. Overall, the article contributes valuable insights and methodologies that could shape the future of automated reasoning and its applications.
Recommendations
- ✓ Further research should explore the generalizability of NL2LOGIC across diverse domains and types of natural language reasoning tasks.
- ✓ Efforts should be made to simplify the implementation of the framework to make it more accessible to practitioners and researchers with varying levels of expertise.
- ✓ Policy discussions should address the ethical and accountability implications of relying on AI-driven reasoning systems in critical domains like law and governance.