Toponym Resolution: Will Prompt Engineering Change Expectations?

Anuradha, Isuri and Sumanathilaka, Deshan Koshala and Mitkov, Ruslan and Rayson, Paul (2025) Toponym Resolution: Will Prompt Engineering Change Expectations? In: Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era :. Incoma Ltd. Shoumen, BULGARIA, Varna, Bulgaria, pp. 95-104.

Abstract

Large Language Models(LLMs) have revolutionised the field of artificial intelligence and have been successfully employed in many disciplines, capturing widespread attention and enthusiasm. Many previous studies have established that Domain-specific Deep Learning models to competitively perform with the general-purpose LLMs (Maatouk et al., 2024;Lu et al., 2024). However, a suitable prompt which provides direct instructions and background information is expected to yield im- proved results (Kamruzzaman and Kim, 2024). The present study focuses on utilising LLMs for the Toponym Resolution task by incorporating Retrieval-Augmented Generation(RAG) and prompting techniques to surpass the results of the traditional Deep Learning models. Moreover, this study demonstrates that promising results can be achieved without relying on large amounts of labelled, domain-specific data. After a descriptive comparison between open-source and proprietary LLMs through different prompt engineering techniques, the GPT-4o model performs best compared to the other LLMs for the Toponym Resolution task.