Ontology population, which aims to extract structured data to enrich domain-specific ontologies from unstructured text, typically faces challenges in terms of data scarcity and linguistic complexity, particularly in specialized fields such as retail banking. In this study, we investigate the application of large language models (LLMs) to populate domain-specific ontologies of retail banking products from Thai corporate documents. We compare traditional span-based approaches to LLMs-based generative methods, with different prompting techniques. Our findings reveal that while span-based methods struggle with data scarcity and the complex linguistic structure, LLMs-based generative approaches substantially outperform, achieving a 61.05% F1 score, with the most improvement coming from providing examples in the prompts. This improvement highlights the potential of LLMs for ontology population tasks, offering a scalable and efficient solution for structured information extraction in especially in low-resource language settings.

Large Language Models (LLMs) often struggle with hallucinations and outdated information. To address this, Information Retrieval (IR) systems can be employed to augment LLMs with up-to-date knowledge. However, existing IR techniques contain deficiencies, posing a performance bottleneck. Given the extensive array of IR systems, combining diverse approaches presents a viable strategy. Nevertheless, prior attempts have yielded restricted efficacy. In this work, we propose an approach that leverages learning-to-rank techniques to combine heterogeneous IR systems. We demonstrate the method on two Retrieval Question Answering (ReQA) tasks. Our empirical findings exhibit a significant performance enhancement, outperforming previous approaches and achieving state-of-the-art results on ReQA SQuAD.