Pektezol, A.S.Ulugergerli, A.B.Öztoklu, V.Demir, Şeniz2025-02-052025-02-0520249798350365887https://doi.org/10.1109/UBMK63289.2024.10773605https://hdl.handle.net/20.500.11779/2497The extraction of entity mentions in a text (named entity recognition) has been traditionally formulated as a sequence labeling problem. In recent years, this approach has evolved from recognizing entities to answering formulated questions related to entity types. The questions, constructed as prompts, are used to elicit desired entity mentions and their types from large language models. In this work, we investigated prompt engineering in Turkish named entity recognition and studied two prompting strategies to guide pretrained language models toward correctly identifying mentions. In particular, we examined the impact of zero-shot and few-shot prompting on the recognition of Turkish named entities by conducting experiments on two large language models. Our evaluations using different prompt templates revealed promising results and demonstrated that carefully constructed prompts can achieve high accuracy on entity recognition, even in languages with complex morphology. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessNamed Entity RecognitionPrompt EngineeringTurkishConference Object10.1109/UBMK63289.2024.107736052-s2.0-85215509605