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It is found that providing candidate senses, similar to what is done in traditional WSD, improves WiC performance in all settings and helps models make more consistent and targeted judgements.
Training on synthetically generated data can significantly boost both the diversity and quality of commonsense reasoning in LLMs, outperforming models trained on scarce human-annotated data.
