SEF-CLGC at SemEval-2026 Task 11: Logical Notation Impact on Language Model Performance
SEF-CLGC combines logical notation with small language models, achieving a 27.80% content score.
Key Findings
Methodology
The SEF-CLGC framework integrates formal logical notations with small language models (SLMs) to evaluate reasoning performance in SemEval-2026 Task 11. The framework involves translating natural language into First-Order Logic (FOL) and then converting it into various logical notations like CLIF and CGIF. These notations help models better understand and handle complex logical reasoning tasks.
Key Results
- The best model achieved a content score of 27.80% on the task, significantly reducing content bias in reasoning.
- The NL-CLIF model performed exceptionally well on the FOLIO dataset, demonstrating the effectiveness of symbolic representation.
- MINIFOL2 performed poorly due to its uncommon symbol combinations.
Significance
This study demonstrates that by combining natural language and formal logical notations, small language models can achieve significant performance improvements in reasoning tasks. This provides new methodologies for academia and new approaches for industry, especially in resource-constrained scenarios.
Technical Contribution
Technical contributions include the introduction of new notations like CLINGO and MINIFOL2, enhancing the flexibility and adaptability of the SEF-CLGC framework. These notations enable models to perform logical reasoning more effectively, reducing content bias.
Novelty
This is the first to combine multiple logical notations in small language models for reasoning tasks, especially demonstrating effectiveness in resource-limited scenarios. Compared to existing methods, it offers a more cost-effective solution.
Limitations
- Reliance on commercial models for NL to FOL translation may affect result stability.
- Uncommon symbol combinations in MINIFOL2 led to poor model performance.
Future Work
Future research could explore the impact of more uncommon notations on model reasoning capabilities and performance on larger datasets.
AI Executive Summary
In the current research landscape of language models, effectively performing logical reasoning remains a challenge. Existing large language models, while excelling in many tasks, still exhibit content bias in logical reasoning tasks.
The SEF-CLGC framework proposed in this paper significantly enhances reasoning task performance by integrating formal logical notations with small language models. Specifically, the framework utilizes various logical notations like CLIF and CGIF to help models better understand and handle complex logical reasoning tasks.
Experimental results show that the best model achieved a content score of 27.80% in SemEval-2026 Task 11, significantly reducing content bias in reasoning. This research not only provides new methodologies for academia but also offers new approaches for industry in handling complex reasoning tasks, especially in resource-constrained scenarios. Future research could explore the impact of more uncommon notations on model reasoning capabilities.
Deep Analysis
Background
In recent years, language models have made significant progress in the field of natural language processing. However, in logical reasoning tasks, especially those involving complex logical structures, existing models still face challenges. Many studies attempt to enhance model reasoning capabilities by integrating symbolic and natural languages.
Core Problem
The core problem is how to effectively translate logical structures in natural language into a form that models can understand and process. Existing methods often exhibit content bias when handling complex logical reasoning, leading to inaccurate reasoning results.
Innovation
The innovation of this paper lies in introducing multiple logical notations like CLIF and CGIF. These notations help models better understand and handle complex logical reasoning tasks. This approach not only enhances model reasoning capabilities but also reduces content bias.
Methodology
- �� Use OpenAI's ChatGPT 5.2 to translate natural language into FOL.
- �� Utilize the SEF-CLGC framework to convert FOL into various logical notations.
- �� Train and evaluate on small language models, comparing the effects of different notations.
Experiments
Experiments used the SemEval-2026 Task 11 dataset, divided into training, validation, and test sets. Models were trained under different notations to evaluate their performance in reasoning tasks. Key metrics included accuracy and content score.
Results
Experimental results show that small language models combining natural language and symbolic representations perform excellently in reasoning tasks, with the best model achieving a content score of 27.80%. Different notations significantly impact model reasoning capabilities.
Applications
This method can be applied to scenarios requiring complex logical reasoning, such as legal document analysis and hypothesis verification in scientific research. By combining symbolic representations, models can perform reasoning more accurately.
Limitations & Outlook
The current method relies on commercial models for NL to FOL translation, which may affect result stability. Additionally, some notations are uncommon, leading to poor model performance. Future research could explore the impact of more uncommon notations on model reasoning capabilities.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. Natural language is like the ingredients, and logical notation is like the spices. Existing large language models are like a chef with many ingredients but not necessarily knowing how to use the spices well. The SEF-CLGC framework is like an experienced chef who knows how to perfectly combine ingredients and spices to create delicious dishes. By integrating natural language and logical notation, models can better understand and handle complex logical reasoning tasks.
ELI14 Explained like you're 14
Imagine you're playing a puzzle game. The game has many clues (natural language), but to solve the puzzle, you need to find the hidden code (logical notation). Existing large language models are like a player with many clues but not necessarily knowing how to find the code. The SEF-CLGC framework is like an experienced player who knows how to combine clues and codes to solve the puzzle. By integrating natural language and logical notation, models can better understand and handle complex logical reasoning tasks.
Glossary
SEF-CLGC
A framework combining logical notation with small language models to enhance reasoning ability.
Used to evaluate reasoning performance in SemEval-2026 Task 11.
FOL
First-Order Logic, a form of formal logical representation used to describe logical relationships.
Serves as the foundational notation in the SEF-CLGC framework.
CLIF
A logical notation used to enhance model reasoning capabilities.
Used in the SEF-CLGC framework for transformation and evaluation.
MINIFOL2
A custom logical notation combining FOL with Boolean operators.
Used to study the impact of notation on model reasoning capabilities.
Content Bias
Bias in reasoning tasks caused by content, affecting accuracy.
The SEF-CLGC framework aims to reduce this bias.
Open Questions Unanswered questions from this research
- 1 How to validate the effectiveness of the SEF-CLGC framework on larger datasets?
- 2 What is the specific impact of uncommon notations on model reasoning capabilities?
Applications
Immediate Applications
Legal Document Analysis
By combining symbolic representations, models can perform more accurate logical analysis of legal documents.
Long-term Vision
Hypothesis Verification in Scientific Research
In scientific research, models can better verify complex hypotheses through logical notation.
Abstract
This paper revisits our pipeline called Syllogistic Evaluation Framework-Common Logic Grammar Construction (SEF-CLGC). We combine formal logical notations with Small Language Models (SLMs) to evaluate reasoning performance on the SemEval-2026 Task 11 Subtask 1: Disentangling Content and Formal Reasoning in Large Language Models. Our experiments show that by relying solely on SLMs, trained on a combination of natural and symbolic languages, our best model achieves a content score of 27.80% on the task while significantly lowering the content bias in reasoning.