  Large language models have demonstrated the capability to perform on machine
translation when the input is prompted with a few examples (in-context
learning). Translation quality depends on various features of the selected
examples, such as their quality and relevance, but previous work has
predominantly focused on individual features in isolation. In this paper, we
propose a general framework for combining different features influencing
example selection. We learn a regression model, CTQ Scorer (Contextual
Translation Quality), that selects examples based on multiple features in order
to maximize the translation quality. On multiple language pairs and language
models, we show that CTQ Scorer helps significantly outperform random selection
as well as strong single-factor baselines reported in the literature. We also
see an improvement of over 2.5 COMET points on average with respect to a strong
BM25 retrieval-based baseline.
