Impact of Large Language Models on Conversational Translation: Towards Translation Agents

Le résumé fourni par la source

Despite advances in deep learning, conversational translation has continued to face persistent challenges: ambiguity in short sentences, limited data for colloquial language, and the need for culturally-situated, commonsense-based translations. However, Large Language Models (LLMs) are now leveraging their vast background knowledge to bring about a fundamental shift in the field. In this paper, we first review the various research and technological developments that have attempted to realize conversational translation prior to the emergence of LLMs. We then demonstrate that, with appropriate prompting, LLMs can resolve most of the key challenges identified in conversational translation research over the last two decades. Finally, we outline the future challenges that the research community must address in the era of LLMs.

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