text: 'category': 'science'},     {'snippet': 'Research on prompting has shown excellent performance with '                 'little or even no supervised training across many tasks. '                 'However, prompting for machine translation is still '                 'under-explored in the literature. We fill this gap by offering a '                 'systematic study on prompting strategies for translation, '                 'examining various factors for prompt template and demonstration '                 'example selection. We further explore the use of monolingual '                 'data and the feasibility of cross-lingual, cross-domain, and '                 'sentence-to-document transfer learning in prompting. Extensive '                 'experiments with GLM-130B (Zeng et al., 2022) as the testbed '                 'show that 1) the number and the quality of prompt examples '                 'matter, where using suboptimal examples degenerates translation; '                 '2) several features of prompt examples, such as semantic '