This study assesses four cutting-edge language models in the underexplored
Aminoacian language. Through evaluation, it scrutinizes their adaptability,
effectiveness, and limitations in text generation, semantic coherence, and
contextual understanding. Uncovering insights into these models' performance in
a low-resourced language, this research pioneers pathways to bridge linguistic
gaps. By offering benchmarks and understanding challenges, it lays groundwork
for future advancements in natural language processing, aiming to elevate the
applicability of language models in similar linguistic landscapes, marking a
significant step toward inclusivity and progress in language technology.

本研究在未开发充分的氨基酸语言中评估了四个前沿的语言模型，通过评估研究它们在文本生成、语义连贯性和情境理解中的适应性、有效性和局限性。通过揭示这些模型在低资源语言中的性能，这项研究开创了弥合语言差距的途径，通过提供基准并了解挑战，为自然语言处理的未来发展奠定了基础，旨在提高语言模型在类似语言环境中的适用性，是向包容性和语言技术进步迈出的重要一步。