BERTurk

Supervised vs. in-context learning for Turkish multiword expression classification

A new arXiv paper evaluates Turkish idiomatic light verb construction (LVC) detection as a binary classification task, comparing a supervised BERTurk baseline against three instruction-tuned LLMs under zero-shot, one-shot, and few-shot prompting. Results show LLMs have very low LVC recall in zero-shot but improve substantially with demonstrations, though one-shot prompting can introduce strong model-specific biases. The supervised baseline remains competitive, while carefully constructed few-shot prompts allow GPT-OSS-20B and Qwen 2.5-14B to match or exceed it. The study highlights significant prompt sensitivity in Turkish metalinguistic classification tasks.