GigitAI at SemEval-2026 Task 11: Hybrid Symbolic-Neural Approach for Syllogistic Validity Classification

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

Summary

GigitAI's system for SemEval-2026 Task 11, which classifies syllogism logical validity, is presented. The primary challenge is that language models often judge arguments based on real-world plausibility rather than strict logical entailment. Direct prompting of six models, including GPT-4o, GPT-5.2, o3, o3-mini, Claude Opus 4.6, and Claude Sonnet 4, with three strategies, achieved a maximum accuracy of 89.5%. GigitAI's best-performing system employs a hybrid approach: GPT-4o-mini extracts the logical structure, followed by deterministic rules for validity checking. This rule-based component is enhanced with bidirectional premise checking, predicate negation post-processing, and a targeted fallback for double negation. This hybrid system achieved 98.95% accuracy on Subtask 1 (combined score 57.74) and 85.8% validity accuracy on Subtask 2. Interestingly, content abstraction degraded performance, suggesting semantic content provides crucial parsing scaffolding despite introducing bias.

Key takeaway

For NLP Engineers developing logical reasoning systems, relying solely on large language models for syllogistic validity classification is insufficient due to their inherent real-world bias. You should integrate a hybrid approach where an LLM like GPT-4o-mini extracts logical structure, followed by deterministic rule-based verification. This strategy significantly boosts accuracy, achieving 98.95% on Subtask 1, and mitigates the LLM's tendency to prioritize semantic plausibility over strict logical entailment.

Key insights

LLMs struggle with logical validity due to real-world bias; hybrid symbolic-neural systems achieve superior accuracy.

Principles

• LLMs prioritize real-world truth over logical entailment.
• Semantic content aids parsing, despite introducing bias.
• Hybrid symbolic-neural systems enhance logical reasoning.

Method

A two-part system: GPT-4o-mini extracts logical structure, then deterministic rules (with bidirectional premise checking, predicate negation, double negation fallback) verify validity.

In practice

• Use LLMs for initial logical structure extraction.
• Implement rule-based logic for validity verification.
• Be cautious with content abstraction in reasoning tasks.

Topics

• Syllogistic Reasoning
• Logical Validity Classification
• Hybrid AI Systems
• Large Language Models
• SemEval-2026 Task 11
• Rule-Based Reasoning

Best for: AI Scientist, NLP Engineer, Research Scientist

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