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ITLC at SemEval-2026 Task 11: Normalization and Deterministic Parsing for Formal Reasoning in LLMs

arXiv – CS AI|Wicaksono Leksono Muhamad, Joanito Agili Lopo, Tack Hwa Wong, Muhammad Ravi Shulthan Habibi, Samuel Cahyawijaya|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed a new method to reduce content biases in large language models' reasoning tasks by transforming syllogisms into canonical logical representations with deterministic parsing. The approach achieved top-5 rankings on the multilingual SemEval-2026 Task 11 benchmark while offering a competitive alternative to complex fine-tuning methods.

Key Takeaways

→New method reduces content effects and biases in LLM reasoning through structural abstraction and deterministic parsing.
→Approach transforms syllogisms into canonical logical representations to improve validity determination.
→Achieved top-5 rankings across all subtasks in the SemEval-2026 Task 11 multilingual benchmark.
→Offers competitive performance compared to complex fine-tuning or activation-level interventions.
→Method particularly addresses reasoning challenges in multi-lingual contexts for large language models.

#llm #reasoning #multilingual #benchmark #logical-parsing #bias-reduction #nlp #semeval

Read Original →via arXiv – CS AI

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