arXiv Computation and Language August 27, 2026 By Yufeng Wu, Meichun Liu

Cross-Dataset Stability of Expert-Informed Skill Prompting and Fine-Tuning for Chinese Metaphor Identification

Read the original on arXiv Computation and Language →

The study compares four approaches for Chinese sentence-level metaphor identification: BERT fine‑tuning, QLoRA-based large language model fine‑tuning, zero‑shot LLM prompting, and zero‑shot prompting with an expert‑informed procedural Skill. Results show that fine‑tuning yields the highest accuracy on the native test set, while the Skill‑based zero‑shot method provides the most stable performance across three datasets, achieving the highest external floor and the smallest performance range. Adding the Skill reduces false positives on one dataset but increases false negatives on others, indicating a trade‑off between precision and recall.

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