Sabancigroup4 at SemEval-2026 Task 5: Uncertainty-Aware Semantic Plausibility Scoring via GNLL Regression and LLM Rationales

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

Summary

Sabancigroup4 participated in SemEval-2026 Task 5, a shared challenge focused on automatically rating the semantic plausibility of ambiguous homonyms within specific contexts. The task involved predicting plausibility scores that aligned with the mean ratings provided by 5 human annotators for a dataset comprising precontext, sentence, and ending combinations. Their proposed system employs an uncertainty-aware training strategy utilizing GNLL regression, coupled with semantic context enrichment derived from Part-of-Speech (POS) tags and Large Language Model (LLM) rationales. This approach frames disambiguation as a probabilistic distribution over multiple meanings, moving beyond traditional single-label selection. The system achieved competitive results, demonstrating 90% accuracy within standard deviation and an 81% Spearman correlation, securing ninth place on the task leaderboard.

Key takeaway

For NLP Engineers developing systems for semantic disambiguation, you should explore uncertainty-aware training strategies like GNLL regression. This approach, which frames disambiguation as a probabilistic distribution rather than single-label selection, can yield more robust and nuanced plausibility scores. Consider integrating LLM rationales and POS tags for richer semantic context, potentially improving your system's accuracy and correlation in tasks involving ambiguous language understanding.

Key insights

Uncertainty-aware GNLL regression and LLM rationales enhance semantic plausibility scoring for ambiguous homonyms.

Principles

• Disambiguation benefits from probabilistic framing.
• Uncertainty-aware training boosts performance.
• LLM rationales enrich semantic context.

Method

The method uses GNLL regression for uncertainty-aware training, predicting a probabilistic distribution of plausibility. It enriches semantic context with POS tags and LLM rationales.

In practice

• Apply GNLL regression for probabilistic outputs.
• Integrate LLM rationales for context enrichment.
• Utilize POS tags for semantic features.

Topics

• Semantic Plausibility
• Homonym Disambiguation
• GNLL Regression
• LLM Rationales
• Natural Language Processing
• SemEval-2026

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.