这项研究揭示了提示设计对多词表达分类的关键影响，做自然语言处理尤其是低资源语言语义分析的团队值得关注，直接参考其示例构建策略可提升模型效果。
该研究针对土耳其语中具有歧义的轻动词结构（LVC）进行分类，区分其字面义与习语义。研究者对比了监督学习模型（BERTurk）与三种指令微调大语言模型（LLM）在零样本、单样本和少样本提示下的表现。结果显示，零样本下LLM对LVC召回率低，单样本提示虽提升检测但引入模型特定偏差，而少样本提示能改善校准。最终，监督基线仍具竞争力，但精心设计的示例可使LLM在LVC分类上达到或超越监督模型。
Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification
Turkish idiomatic light verb constructions (LVCs) are challenging for multiword expression processing because they often share the same surface form as fully literal verb-object combinations while functioning as a single, partially idiomatic predicate. We frame Turkish LVC detection as a binary classification task (literal meaning vs. idiomatic meaning) and evaluate on a manually created controlled set (N=147) with matched negatives: out-of-domain random sentences and in-domain literal controls (NLVC), alongside LVC positives. We compare a supervised Turkish encoder baseline (BERTurk with a classifier head) to three instruction-tuned LLMs from different families under zero-shot, one-shot, and few-shot prompting, and analyze how demonstrations shift error profiles. In zero-shot, LLMs perform well on negatives but show very low LVC recall. One-shot prompting sharply improves LVC detection but can induce strong, model-specific biases, leading models to overpredict or underpredict LVCs. A richer few-shot prompt improves calibration and yields robust overall performance for GPT-OSS-20B and Qwen 2.5-14B. Overall, the results highlight substantial prompt sensitivity in Turkish metalinguistic classification: the supervised baseline remains competitive, while prompted LLMs can match or exceed it on LVCs with carefully constructed demonstrations.