@inproceedings{ranaldi-etal-2026-thinking,
title = "Thinking in Schemas: Robust Syllogistic Reasoning in {LLM}s",
author = "Ranaldi, Federico and
Ranaldi, Leonardo and
Zanzotto, Fabio Massimo and
Cohen, Shay B",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1897/",
pages = "40891--40909",
ISBN = "979-8-89176-390-6",
abstract = "LLMs often mistake what sounds true for what is formally valid. This limitation is especially evident in syllogistic reasoning, where plausible arguments can lead models to endorse conclusions that are logically invalid, a phenomenon known as Content Effect (CE).We present Boethius, a schema-guided framework for syllogistic reasoning that disentangles semantic plausibility from logical validity. Boethius adopts an auditable, quasi-formal reasoning process with two complementary stages: a Schema Module, which deduces the underlying logical form by analysing the formal structure of the premises, and an Instantiation Module, which instantiates this form over the concrete argument and evaluates validity independently of content-level semantics.Our results show that Boethius consistently outperforms existing approaches, improving syllogistic reasoning accuracy while substantially reducing CE. These gains hold for both large models in a pure in-context learning setting and smaller models trained via schema-guided trajectories using supervised fine-tuning and optimisation-based refinement."
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<abstract>LLMs often mistake what sounds true for what is formally valid. This limitation is especially evident in syllogistic reasoning, where plausible arguments can lead models to endorse conclusions that are logically invalid, a phenomenon known as Content Effect (CE).We present Boethius, a schema-guided framework for syllogistic reasoning that disentangles semantic plausibility from logical validity. Boethius adopts an auditable, quasi-formal reasoning process with two complementary stages: a Schema Module, which deduces the underlying logical form by analysing the formal structure of the premises, and an Instantiation Module, which instantiates this form over the concrete argument and evaluates validity independently of content-level semantics.Our results show that Boethius consistently outperforms existing approaches, improving syllogistic reasoning accuracy while substantially reducing CE. These gains hold for both large models in a pure in-context learning setting and smaller models trained via schema-guided trajectories using supervised fine-tuning and optimisation-based refinement.</abstract>
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%0 Conference Proceedings
%T Thinking in Schemas: Robust Syllogistic Reasoning in LLMs
%A Ranaldi, Federico
%A Ranaldi, Leonardo
%A Zanzotto, Fabio Massimo
%A Cohen, Shay B.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F ranaldi-etal-2026-thinking
%X LLMs often mistake what sounds true for what is formally valid. This limitation is especially evident in syllogistic reasoning, where plausible arguments can lead models to endorse conclusions that are logically invalid, a phenomenon known as Content Effect (CE).We present Boethius, a schema-guided framework for syllogistic reasoning that disentangles semantic plausibility from logical validity. Boethius adopts an auditable, quasi-formal reasoning process with two complementary stages: a Schema Module, which deduces the underlying logical form by analysing the formal structure of the premises, and an Instantiation Module, which instantiates this form over the concrete argument and evaluates validity independently of content-level semantics.Our results show that Boethius consistently outperforms existing approaches, improving syllogistic reasoning accuracy while substantially reducing CE. These gains hold for both large models in a pure in-context learning setting and smaller models trained via schema-guided trajectories using supervised fine-tuning and optimisation-based refinement.
%U https://aclanthology.org/2026.acl-long.1897/
%P 40891-40909
Markdown (Informal)
[Thinking in Schemas: Robust Syllogistic Reasoning in LLMs](https://aclanthology.org/2026.acl-long.1897/) (Ranaldi et al., ACL 2026)
ACL
- Federico Ranaldi, Leonardo Ranaldi, Fabio Massimo Zanzotto, and Shay B Cohen. 2026. Thinking in Schemas: Robust Syllogistic Reasoning in LLMs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 40891–40909, San Diego, California, United States. Association for Computational Linguistics.