This paper examines ceramic-related cultural texts as a case study,systematically evaluating the capabilities and limitations of two popular large language models(LLMs)when processing culturally embedded content while...This paper examines ceramic-related cultural texts as a case study,systematically evaluating the capabilities and limitations of two popular large language models(LLMs)when processing culturally embedded content while simultaneously developing innovative methodological approaches for technology-enhanced translation classrooms.By conducting comparative analyses of artificial intelligence(AI)-generated translations,the study identifies key challenges in translating ceramic cultural texts,explores potential refinements for machine translation algorithms,and formulates evidence-based teaching strategies that leverage these insights to cultivate comprehensive translation skills.The findings indicate that while LLMs have demonstrated notable effectiveness in basic information transfer and literal semantic comprehension,they currently still need improvements to understand and process specialized jargon as well as metaphors.The findings also offer translation teachers a substantive framework for pedagogical transformation in the digital era,effectively bridging the theoretical divide between cultural translation studies and technological applications in translation education.AI should be leveraged to enhance ceramic culture translation,facilitating the advancement of cross-cultural communication and translation strategies.展开更多
文摘This paper examines ceramic-related cultural texts as a case study,systematically evaluating the capabilities and limitations of two popular large language models(LLMs)when processing culturally embedded content while simultaneously developing innovative methodological approaches for technology-enhanced translation classrooms.By conducting comparative analyses of artificial intelligence(AI)-generated translations,the study identifies key challenges in translating ceramic cultural texts,explores potential refinements for machine translation algorithms,and formulates evidence-based teaching strategies that leverage these insights to cultivate comprehensive translation skills.The findings indicate that while LLMs have demonstrated notable effectiveness in basic information transfer and literal semantic comprehension,they currently still need improvements to understand and process specialized jargon as well as metaphors.The findings also offer translation teachers a substantive framework for pedagogical transformation in the digital era,effectively bridging the theoretical divide between cultural translation studies and technological applications in translation education.AI should be leveraged to enhance ceramic culture translation,facilitating the advancement of cross-cultural communication and translation strategies.
