GheGheGhe at SemEval-2026 Task 11: Decoupling Logic from Belief with Bias-Targeted Fine-Tuning and Neuro-Symbolic Syllogistic Reasoning
Abstract
AbstractThis paper presents a multi-paradigm approach to the first two subtasks of SemEval-2026 Task 11. For the first subtask, we explore two complementary strategies: a Llama-3 8B PEFT Majority Vote Ensemble, trained with bias-targeted augmented data, and a hybrid approach that separates LLM processing from logical reasoning, converting sentences into canonical logical forms for deterministic analysis. The hybrid approach is further extended to the second subtask. Official results placed us 17th in the first subtask and 15th in the second. Post-evaluation analysis indicates that our best model achieved perfect accuracy on the first subtask and revealed several errors in the ground truth data. After identifying certain implementation issues in the second subtask approach, the F1 retrieval score increased to over 98%, which would place us within the top 5 on the leaderboard.