@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Lakksh at SemEval-2026 Task 11(1 2): Neuro-Symbolic Decomposition to Mitigate Content Bias in Syllogistic Reasoning
%A Sharma, Lakksh
%A Sharma, Krish
%A Bedi, Jatin
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F sharma-etal-2026-lakksh
%X 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.
%U https://aclanthology.org/2026.semeval-1.17/
%P 115-120
Markdown (Informal)
[Lakksh at SemEval-2026 Task 11(1 2): Neuro-Symbolic Decomposition to Mitigate Content Bias in Syllogistic Reasoning](https://aclanthology.org/2026.semeval-1.17/) (Sharma et al., SemEval 2026)
ACL