@inproceedings{sharma-etal-2026-lakksh,
    title = "Lakksh at {S}em{E}val-2026 Task 11(1 2): Neuro-Symbolic Decomposition to Mitigate Content Bias in Syllogistic Reasoning",
    author = "Sharma, Lakksh  and
      Sharma, Krish  and
      Bedi, Jatin",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.17/",
    pages = "115--120",
    ISBN = "979-8-89176-414-9",
    abstract = "Syllogistic reasoning is the ability to distinguish logical validity from semantic plausibility {---} a setting in which LLMs succumb to frequent content bias by conflating the two. The result is a characteristic failure to recognize logically valid arguments with highly implausible conclusions and logically invalid but semantically plausible arguments. This paper introduces a neuro-symbolic system that avoids this behavior by design: neural structure extraction is strictly separated from symbolic validity checking. A T5-Small parser is trained only on synthetic nonsense-symbol syllogisms, ensuring that the structural parse is learned in the absence of real-world semantics. Validity checking is performed by a deterministic symbolic kernel operating on extracted logical form alone, ensuring that semantic content cannot influence the final call. In binary validity classification, the system achieves 97.38{\%} accuracy with a Total Content Effect of 3.10; in the retrieval setting, it achieves 82.11{\%} accuracy with 99.47{\%} F1 on premise identification. Ablation experiments show that formal theorem proving via NL-to-Z3 translation actually increases content bias due to leakage in intermediate representations. The results recommend architectural separation as a promising content-robustness strategy for syllogistic reasoning."
}