Automatic Text Generation in Banjar Language Using GPT-4 for Low-Resource Language Preservation

This study explores the capabilities of Generative Pre-trained Transformer 4 (GPT-4), a state-of-the-art Large Language Model (LLM), in generating text in the Banjar language, an under-resourced language from South Kalimantan, Indonesia. Using prompt engineering and both zero-shot and few-shot learning approaches, GPT-4 was guided to understand and generate Banjar text. A parallel dataset and small Banjar corpus were developed for experimentation. Evaluation involved quantitative metrics (Bilingual Evaluation Understudy (BLEU) score, perplexity) and qualitative assessments from five native speakers. The few-shot approach performed best, achieving a BLEU score of 0.56 (std. dev. = $0.04)$, perplexity of 30.8 (std. dev. = 2.1), fluency of 4.2 (std. dev. $=0.3$), and coherence of 4.1 (std. dev. $=0.4$). The zero-shot approach yielded a BLEU score of 0.34 (std. dev. $=0.05$), perplexity of 45.2 (std. dev. $=3.4$), fluency of 3.2 (std. dev. $=0.4$), and coherence of 3.0 (std. dev. $=0.5$). These findings demonstrate that GPT-4, although not trained on Banjar, can generate coherent and natural-sounding text, especially with few-shot prompting. This study contributes to the development of AI technologies for local languages and highlights opportunities for integrating LLMs in cultural preservation efforts.