Ancient Korean Archive Translation: Comparison Analysis on Statistical phrase alignment, LLM in-context learning, and inter-methodological approach
<|reference_start|>Ancient Korean Archive Translation: Comparison Analysis on Statistical phrase alignment, LLM in-context learning, and inter-methodological approach: This study aims to compare three methods for translating ancient texts with sparse corpora: (1) the traditional statistical translation method of phrase alignment, (2) in-context LLM learning, and (3) proposed inter methodological approach - statistical machine translation method using sentence piece tokens derived from unified set of source-target corpus. The performance of the proposed approach in this study is 36.71 in BLEU score, surpassing the scores of SOLAR-10.7B context learning and the best existing Seq2Seq model. Further analysis and discussion are presented.<|reference_end|>
@article{kim2024ancient,
    title={Ancient Korean Archive Translation: Comparison Analysis on Statistical
  phrase alignment, LLM in-context learning, and inter-methodological approach},
    author={Sojung Lucia Kim, Taehong Jang, Joonmo Ahn},
    journal={arXiv preprint arXiv:2407.11368},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11368},
    primaryClass={cs.CL}
}