word-sense-disambiguation

The paper argues that gold-standard labels, not models, have become the bottleneck in English word sense disambiguation, releasing new benchmarks and a bi-encoder model to address this, and demonstrating that relabeling improves performance.

SwanNLP presents an LLM-based framework for plausibility scoring in narrative word sense disambiguation at SemEval-2026 Task 5, using structured reasoning and dynamic few-shot prompting to predict human-perceived plausibility of word senses in short stories. The work demonstrates that commercial large-parameter LLMs with few-shot prompting and model ensembling effectively replicate human judgment patterns in realistic narrative contexts.