NCL-UoR at SemEval-2026 Task 5: Embedding-Based Methods, Fine-Tuning, and LLMs for Word Sense Plausibility Rating

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

Summary

NCL-UoR participated in SemEval-2026 Task 5, focusing on word sense plausibility rating, a task requiring prediction of human-perceived plausibility on a 1–5 scale for ambiguous homonyms within short narrative stories. The team systematically compared three distinct approaches: embedding-based methods utilizing sentence embeddings with standard regressors, transformer fine-tuning incorporating parameter-efficient adaptation, and large language model (LLM) prompting. The most effective system employed a structured prompting strategy that meticulously decomposed the evaluation into specific narrative components—precontext, target sentence, and ending—and applied explicit decision rules for rating calibration. Analysis revealed that this structured prompting approach, combined with decision rules, significantly outperformed both fine-tuned models and embedding-based methods, underscoring that prompt design holds greater importance than model scale for this particular task.

Key takeaway

For NLP engineers developing word sense disambiguation or plausibility rating systems, you should prioritize sophisticated prompt engineering over simply scaling up model size. The findings from SemEval-2026 Task 5 indicate that designing structured prompts that decompose narrative context and incorporate explicit decision rules yields superior performance. Focus your efforts on crafting precise prompts to achieve more accurate and calibrated human-like plausibility judgments, rather than relying solely on larger LLMs.

Key insights

Structured prompting with explicit rules significantly enhances word sense plausibility rating, outweighing model scale.

Principles

• Prompt design is more critical than model scale for word sense plausibility tasks.
• Decomposing complex tasks into narrative components improves LLM performance.
• Explicit decision rules enhance LLM rating calibration and accuracy.

Method

Structured prompting decomposes word sense plausibility evaluation into precontext, target sentence, and ending components, applying explicit decision rules for rating calibration.

In practice

• Implement structured prompts for complex NLP tasks requiring nuanced understanding.
• Incorporate explicit decision rules in LLM-based rating systems for consistency.
• Decompose narrative analysis into contextual segments for improved LLM processing.

Topics

• Word Sense Disambiguation
• Large Language Models
• Prompt Engineering
• SemEval-2026
• Transformer Fine-tuning
• Embedding-Based Methods

Best for: AI Engineer, 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.