@inproceedings{akl-etal-2026-sef,
title = "{SEF}-{CLGC} at {S}em{E}val-2026 Task 11: Logical Notation Impact on Language Model Performance",
author = "Akl, Hanna Abi and
Gandon, Fabien and
Faron, Catherine and
Monnin, Pierre",
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-1.285/",
pages = "2251--2261",
ISBN = "979-8-89176-414-9",
abstract = "This paper revisits our pipeline called Syllogistic Evaluation Framework-Common Logic Grammar Construction (SEF-CLGC). We combine formal logical notations with Small Language Models (SLMs) to evaluate reasoning performance on the SemEval-2026 Task 11 Subtask 1: Disentangling Content and Formal Reasoning in Large Language Models. Our experiments show that by relying solely on SLMs, trained on a combination of natural and symbolic languages, our best model achieves a content score of 27.80{\%} on the task while significantly lowering the content bias in reasoning."
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<abstract>This paper revisits our pipeline called Syllogistic Evaluation Framework-Common Logic Grammar Construction (SEF-CLGC). We combine formal logical notations with Small Language Models (SLMs) to evaluate reasoning performance on the SemEval-2026 Task 11 Subtask 1: Disentangling Content and Formal Reasoning in Large Language Models. Our experiments show that by relying solely on SLMs, trained on a combination of natural and symbolic languages, our best model achieves a content score of 27.80% on the task while significantly lowering the content bias in reasoning.</abstract>
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%0 Conference Proceedings
%T SEF-CLGC at SemEval-2026 Task 11: Logical Notation Impact on Language Model Performance
%A Akl, Hanna Abi
%A Gandon, Fabien
%A Faron, Catherine
%A Monnin, Pierre
%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 akl-etal-2026-sef
%X This paper revisits our pipeline called Syllogistic Evaluation Framework-Common Logic Grammar Construction (SEF-CLGC). We combine formal logical notations with Small Language Models (SLMs) to evaluate reasoning performance on the SemEval-2026 Task 11 Subtask 1: Disentangling Content and Formal Reasoning in Large Language Models. Our experiments show that by relying solely on SLMs, trained on a combination of natural and symbolic languages, our best model achieves a content score of 27.80% on the task while significantly lowering the content bias in reasoning.
%U https://aclanthology.org/2026.semeval-1.285/
%P 2251-2261
Markdown (Informal)
[SEF-CLGC at SemEval-2026 Task 11: Logical Notation Impact on Language Model Performance](https://aclanthology.org/2026.semeval-1.285/) (Akl et al., SemEval 2026)
ACL