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YNU-NLP at SemEval-2026 Task 11: A Neuro-Symbolic Approach with Reflexion Mechanism Disentangling Content and Formal Reasoning in Language Models

Year: 2026
Type: conference-paper
Abstract: This paper describes our systems for SemEval-2026 Task 11, Disentangling Content and Formal Reasoning in Language Models.We participated in all four subtasks, addressing the Content Effect-a phenomenon where models rely on real-world plausibility rather than logical validity.Existing methods, such as standard Chain-of-Thought (CoT) prompting or single-task Supervised Fine-Tuning (SFT), often struggle to completely decouple content from reasoning due to the inherent probabilistic biases in pre-trained models.To address these limitations, a hybrid neuro-symbolic framework based on the Qwen2.5-14Barchitecture is proposed, integrating multi-task instruction tuning with a robust neuro-symbolic pipeline.The principal innovation lies in the deployment of a Reflexion mechanism coupled with formal verification: natural language arguments are parsed into First-Order Logic (FOL) and subsequently verified by the Z3 Theorem Prover.Parsing anomalies are automatically rectified through an iterative self-correction module.The proposed system ranked 1st in Subtasks 1 & 2, 2nd in Subtask 4, and 9th in Subtask 3, validating its ability to decouple logical validity from content plausibility.To strictly ensure replicability, the dataset and codebase are available at https://github. com/DALUOLUODE/Semeval_2026_task11.
DOI: 10.18653/v1/2026.semeval-1.126
Institution: Yunnan University
Language: English
Open Access status: hybrid
OpenAlex ID: W7166830149
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