ITLC at SemEval-2026 Task 11: Normalization and Deterministic Parsing for Formal Reasoning in LLMs
arXiv:2603.02676v1 Announce Type: new Abstract: Large language models suffer from content effects in reasoning tasks, particularly in multi-lingual contexts. We introduce a novel method that reduces these biases through explicit structural abstraction that transforms syllogisms into canonical logical representations and applies deterministic parsing to determine validity. Evaluated on the SemEval-2026 Task 11 multilingual benchmark, our approach achieves top-5 rankings across all subtasks while substantially reducing content effects and offering a competitive alternative to complex fine-tuning or activation-level interventions.
arXiv:2603.02676v1 Announce Type: new Abstract: Large language models suffer from content effects in reasoning tasks, particularly in multi-lingual contexts. We introduce a novel method that reduces these biases through explicit structural abstraction that transforms syllogisms into canonical logical representations and applies deterministic parsing to determine validity. Evaluated on the SemEval-2026 Task 11 multilingual benchmark, our approach achieves top-5 rankings across all subtasks while substantially reducing content effects and offering a competitive alternative to complex fine-tuning or activation-level interventions.
Executive Summary
This article presents a novel method to mitigate content effects in large language models (LLMs) through explicit structural abstraction and deterministic parsing. The approach transforms syllogisms into canonical logical representations to determine validity, achieving top-5 rankings in SemEval-2026 Task 11 multilingual benchmark. The method offers a competitive alternative to complex fine-tuning or activation-level interventions, substantially reducing content effects. The study's findings have significant implications for the development of more accurate and unbiased LLMs in reasoning tasks.
Key Points
- ▸ The proposed method addresses content effects in LLMs through explicit structural abstraction.
- ▸ Deterministic parsing is used to determine validity in canonical logical representations.
- ▸ The approach achieves top-5 rankings in SemEval-2026 Task 11 multilingual benchmark.
Merits
Strength in Addressing Content Effects
The proposed method explicitly addresses content effects in LLMs, a significant issue in reasoning tasks, particularly in multilingual contexts. By transforming syllogisms into canonical logical representations, the approach reduces the influence of content biases and improves the accuracy of LLMs.
Demerits
Complexity and Generalizability
The proposed method may be complex to implement, especially for non-experts in natural language processing and logical reasoning. Further research is needed to evaluate the generalizability of the approach across different LLM architectures and reasoning tasks.
Expert Commentary
The proposed method is a significant contribution to the field of natural language processing and logical reasoning. However, its practicality and generalizability need to be evaluated further. The study's findings have significant implications for the development of more accurate and unbiased LLMs, which can inform policy decisions related to the use of LLMs in critical applications. The integration of the proposed method into existing LLM frameworks can improve their accuracy and reduce content effects in reasoning tasks. Additionally, the study highlights the importance of addressing content effects in LLMs, a significant issue in reasoning tasks.
Recommendations
- ✓ Further research is needed to evaluate the generalizability of the proposed method across different LLM architectures and reasoning tasks.
- ✓ The development of more comprehensive mitigation strategies for biases in LLMs is essential to ensure their accurate and unbiased performance in critical applications.
