Team ewelinaksiez at SemEval-2026 Task 11: Reducing Content Bias in Syllogistic Reasoning via Semantic Abstraction

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

Summary

Team ewelinaksiez presented a system for SemEval-2026 Task 11 Subtask 1, focusing on content-independent syllogistic reasoning. The task evaluates language models' ability to determine logical argument validity irrespective of semantic plausibility. Their proposed solution is a data augmentation strategy that progressively abstracts lexical semantics by replacing content words with symbolic placeholders and pseudo-words, while preserving logical structure. Fine-tuning microsoft/deberta-large-mnli with this abstraction-based augmentation reduced Content Effect and improved accuracy, achieving competitive performance. However, the system showed substantial sensitivity to random initialization. A layer-wise probing analysis using a Minimum Description Length framework revealed that the approach decreased plausibility information accessibility in later transformer layers, indicating a shift towards structure-oriented reasoning.

Key takeaway

For NLP Engineers developing language models for logical reasoning, particularly in content-independent tasks, consider implementing semantic abstraction as a data augmentation strategy. This approach can mitigate content-driven biases and enhance formal validity assessment. Be mindful of the observed sensitivity to random initialization during model training and evaluation, and conduct thorough testing across multiple seeds to ensure robust performance and reliable results.

Key insights

Semantic abstraction effectively reduces content bias in language models for syllogistic reasoning tasks.

Principles

• Content bias impacts language model logical reasoning.
• Abstraction can shift LMs towards structure-oriented reasoning.
• LM evaluation outcomes are sensitive to random initialization.

Method

A data augmentation strategy progressively abstracts lexical semantics by replacing content words with symbolic placeholders and pseudo-words, while preserving logical structure.

In practice

• Apply semantic abstraction for bias reduction.
• Fine-tune microsoft/deberta-large-mnli for reasoning tasks.
• Conduct layer-wise probing for reasoning analysis.

Topics

• Syllogistic Reasoning
• Content Bias
• Semantic Abstraction
• Data Augmentation
• Language Models
• DeBERTa
• Transformer Layers

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

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