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Computer Science > Computation and Language

arXiv:2405.02318 (cs)
[Submitted on 18 Apr 2024 (v1), last revised 3 Mar 2025 (this
version, v2)]

Title:NL2FOL: Translating Natural Language to First-Order Logic for
Logical Fallacy Detection

Authors:Abhinav Lalwani, Tasha Kim, Lovish Chopra, Christopher Hahn,
Zhijing Jin, Mrinmaya Sachan
View a PDF of the paper titled NL2FOL: Translating Natural Language
to First-Order Logic for Logical Fallacy Detection, by Abhinav
Lalwani and 4 other authors
View PDF

    Abstract:Translating natural language into formal language such
    as First-Order Logic (FOL) is a foundational challenge in NLP
    with wide-ranging applications in automated reasoning,
    misinformation tracking, and knowledge validation. In this paper,
    we introduce Natural Language to First-Order Logic (NL2FOL), a
    framework to autoformalize natural language to FOL step by step
    using Large Language Models (LLMs). Our approach addresses key
    challenges in this translation process, including the integration
    of implicit background knowledge. By leveraging structured
    representations generated by NL2FOL, we use Satisfiability Modulo
    Theory (SMT) solvers to reason about the logical validity of
    natural language statements. We present logical fallacy detection
    as a case study to evaluate the efficacy of NL2FOL. Being
    neurosymbolic, our approach also provides interpretable insights
    into the reasoning process and demonstrates robustness without
    requiring model fine-tuning or labeled training data. Our
    framework achieves strong performance on multiple datasets. On
    the LOGIC dataset, NL2FOL achieves an F1-score of 78%, while
    generalizing effectively to the LOGICCLIMATE dataset with an
    F1-score of 80%.

          Computation and Language (cs.CL); Artificial Intelligence
Subjects: (cs.AI); Machine Learning (cs.LG); Logic in Computer
          Science (cs.LO)
Cite as:  arXiv:2405.02318 [cs.CL]
          (or arXiv:2405.02318v2 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2405.02318
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Abhinav Lalwani [view email]
[v1] Thu, 18 Apr 2024 00:20:48 UTC (106 KB)
[v2] Mon, 3 Mar 2025 00:38:48 UTC (1,297 KB)
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    View a PDF of the paper titled NL2FOL: Translating Natural
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    Abhinav Lalwani and 4 other authors
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