@inproceedings{chen-etal-2026-sylloscope,
title = "Sylloscope at {S}em{E}val-2026 Task 11: Decoupling Logic from Belief via {D}eep{S}eek-Enhanced Distillation in Qwen Models",
author = "Chen, Zhanyu and
Mu{\~n}oz Mart{\'i}n, Mar{\'i}a Teresa and
Huisman, Sem and
Lan, Jingjing",
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-1.184/",
pages = "1421--1428",
ISBN = "979-8-89176-414-9",
abstract = "This paper presents our approach for SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. We propose a neuro-symbolic teacher-student framework that utilizes DeepSeek-R1 as a Logical Auditor to generate a high-fidelity training corpus. We distill these analytical behaviors into Qwen-3 models using Low Rank Adaptation (LoRA), focusing on teaching the mechanics of logic rather than simple label matching. Our system yields robust results across both subtasks, with a ranking score of 39.81 (96.86{\%} accuracy) on Subtask 1 and 26.02 on Subtask 3. However, validity bias partially persists, so we conclude that while structured distillation substantially mitigates belief bias, fully disentangling logical validity from plausibility remains a central challenge for future development."
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<abstract>This paper presents our approach for SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. We propose a neuro-symbolic teacher-student framework that utilizes DeepSeek-R1 as a Logical Auditor to generate a high-fidelity training corpus. We distill these analytical behaviors into Qwen-3 models using Low Rank Adaptation (LoRA), focusing on teaching the mechanics of logic rather than simple label matching. Our system yields robust results across both subtasks, with a ranking score of 39.81 (96.86% accuracy) on Subtask 1 and 26.02 on Subtask 3. However, validity bias partially persists, so we conclude that while structured distillation substantially mitigates belief bias, fully disentangling logical validity from plausibility remains a central challenge for future development.</abstract>
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%0 Conference Proceedings
%T Sylloscope at SemEval-2026 Task 11: Decoupling Logic from Belief via DeepSeek-Enhanced Distillation in Qwen Models
%A Chen, Zhanyu
%A Muñoz Martín, María Teresa
%A Huisman, Sem
%A Lan, Jingjing
%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 chen-etal-2026-sylloscope
%X This paper presents our approach for SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. We propose a neuro-symbolic teacher-student framework that utilizes DeepSeek-R1 as a Logical Auditor to generate a high-fidelity training corpus. We distill these analytical behaviors into Qwen-3 models using Low Rank Adaptation (LoRA), focusing on teaching the mechanics of logic rather than simple label matching. Our system yields robust results across both subtasks, with a ranking score of 39.81 (96.86% accuracy) on Subtask 1 and 26.02 on Subtask 3. However, validity bias partially persists, so we conclude that while structured distillation substantially mitigates belief bias, fully disentangling logical validity from plausibility remains a central challenge for future development.
%U https://aclanthology.org/2026.semeval-1.184/
%P 1421-1428
Markdown (Informal)
[Sylloscope at SemEval-2026 Task 11: Decoupling Logic from Belief via DeepSeek-Enhanced Distillation in Qwen Models](https://aclanthology.org/2026.semeval-1.184/) (Chen et al., SemEval 2026)
ACL