  Large language models (LLMs) have demonstrated impressive capabilities in
general scenarios, exhibiting a level of aptitude that approaches, in some
aspects even surpasses, human-level intelligence. Among their numerous skills,
the translation abilities of LLMs have received considerable attention.
Compared to typical machine translation that focuses solely on source-to-target
mapping, LLM-based translation can potentially mimic the human translation
process which might take preparatory steps to ensure high-quality translation.
This work explores this possibility by proposing the MAPS framework, which
stands for Multi-Aspect Prompting and Selection. Specifically, we enable LLMs
first to analyze the given source sentence and induce three aspects of
translation-related knowledge: keywords, topics, and relevant demonstrations to
guide the final translation process. Moreover, we employ a selection mechanism
based on quality estimation to filter out noisy and unhelpful knowledge. Both
automatic (3 LLMs x 11 directions x 2 automatic metrics) and human evaluation
(preference study and MQM) demonstrate the effectiveness of MAPS. Further
analysis shows that by mimicking the human translation process, MAPS reduces
various translation errors such as hallucination, ambiguity, mistranslation,
awkward style, untranslated text, and omission. Source code is available at
https://github.com/zwhe99/MAPS-mt.
