AICOE-Tredence at SemEval-2026 Task 11: Mitigating Content Bias in Syllogisms via Symbolic Logic-Language Decoupling

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

AICOE-Tredence's submission to SemEval-2026 Task 11 addresses content bias in large language models (LLMs) during multilingual syllogistic reasoning. Their proposed "structure-first reasoning paradigm" abstracts natural language syllogisms into Aristotelian logical forms, mapping arguments to mood–figure representations. This method classifies validity in a symbolic space, effectively removing semantic content from the reasoning process. Utilizing Gemini-3 Pro Preview, their approach achieved a perfect combined score on the private test sets of Subtasks 1 and 3, demonstrating 100% validity accuracy and zero content bias in both English and multilingual contexts. The research also explored transferring this paradigm to smaller models through structural supervision, which successfully retained high accuracy with minimal bias.

Key takeaway

For NLP Engineers developing robust reasoning systems, this research suggests explicitly decoupling logical form from linguistic content. If you are struggling with content bias or cross-lingual performance in LLMs, consider implementing a structure-first reasoning paradigm. This approach, demonstrated with 100% accuracy and zero bias using Gemini-3 Pro Preview, can significantly enhance the reliability and fairness of your models, even when distilled to smaller systems.

Key insights

Explicitly separating logical form from linguistic content mitigates content bias and improves cross-lingual robustness in LLM reasoning.

Principles

• LLMs conflate logical validity with real-world plausibility.
• Structure-first reasoning improves bias-resilient LLM reasoning.
• Symbolic logic classification removes semantic content.

Method

Abstract natural language syllogisms into Aristotelian logical forms, map arguments to mood–figure representations, then classify validity in this symbolic space.

In practice

• Apply structural supervision to smaller models.
• Use symbolic logic for bias-resilient reasoning.
• Decouple logic from language for cross-lingual tasks.

Topics

• Content Bias Mitigation
• Syllogistic Reasoning
• Large Language Models
• Symbolic Logic
• SemEval-2026
• Structural Supervision

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.