  This study evaluates the machine translation (MT) quality of two
state-of-the-art large language models (LLMs) against a tradition-al neural
machine translation (NMT) system across four language pairs in the legal
domain. It combines automatic evaluation met-rics (AEMs) and human evaluation
(HE) by professional transla-tors to assess translation ranking, fluency and
adequacy. The re-sults indicate that while Google Translate generally
outperforms LLMs in AEMs, human evaluators rate LLMs, especially GPT-4,
comparably or slightly better in terms of producing contextually adequate and
fluent translations. This discrepancy suggests LLMs' potential in handling
specialized legal terminology and context, highlighting the importance of human
evaluation methods in assessing MT quality. The study underscores the evolving
capabil-ities of LLMs in specialized domains and calls for reevaluation of
traditional AEMs to better capture the nuances of LLM-generated translations.
