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Semiotic Relations and Proof Methods: A Cross-Genre Study of Argument Structure with Large Language Models

arXiv 2609.15194 ↗First posted 14 Sep 2026cs.CLcited by 0

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Abstract (arXiv)

When a direct proof of a statement S seems hard or even impossible to obtain, there may exist another statement (or set of statements) S*, somehow related to S, on the basis of which S can be proved. In order to investigate what options can be used to move from S to S*, four kinds of semiotic relations inspired by the four master tropes of semiotic research are briefly reviewed. Specifically, our syntagmatic, paradigmatic, antithetic and meronymic relations correspond, respectively, to metonymy, metaphor, irony and synecdoche. It is suggested that these four semiotic relations determine the options to move from S to S*, leading to proof by inference, proof by analogy, proof by contradiction, and proof by case analysis. To examine how the four relations are actually used across different kinds of argument, we complement the framework with an empirical study. We turn the four relations into explicit operational definitions and apply them to a cross-genre corpus of mathematical, legal, and everyday argument using a panel of large language models. We find that the relations are used very unevenly across genres: mathematical proofs draw on all four, whereas legal and everyday reasoning rely almost entirely on inference.

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References · 12

  1. 2020Generative Language Modeling for Automated Theorem Provingcited by 388
  2. 2022Emergent Analogical Reasoning in Large Language Modelscited by 240
  3. 2023ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learningcited by 100
  4. 2021NaturalProofs: Mathematical Theorem Proving in Natural Languagecited by 81
  5. 2025The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMscited by 74
  6. 2022NaturalProver: Grounded Mathematical Proof Generation with Language Modelscited by 72
  7. 2024A Survey on Deep Learning for Theorem Provingcited by 57
  8. 2022Mining Legal Arguments in Court Decisionscited by 35
  9. 2024Evaluating the Robustness of Analogical Reasoning in Large Language Modelscited by 31
  10. 2025Characterizing the Investigative Methods of Fictional Detectives with Large Language Modelscited by 3
  11. 2026Towards Solving the Gilbert-Pollak Conjecture via Large Language Modelscited by 2
  12. 2026Revisiting Northrop Frye's Four Myths Theory with Large Language Modelscited by 1

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