  The quality of output from large language models (LLMs), particularly in
machine translation (MT), is closely tied to the quality of in-context examples
(ICEs) provided along with the query, i.e., the text to translate. The
effectiveness of these ICEs is influenced by various factors, such as the
domain of the source text, the order in which the ICEs are presented, the
number of these examples, and the prompt templates used. Naturally, selecting
the most impactful ICEs depends on understanding how these affect the resulting
translation quality, which ultimately relies on translation references or human
judgment. This paper presents a novel methodology for in-context learning (ICL)
that relies on a search algorithm guided by domain-specific quality estimation
(QE). Leveraging the XGLM model, our methodology estimates the resulting
translation quality without the need for translation references, selecting
effective ICEs for MT to maximize translation quality. Our results demonstrate
significant improvements over existing ICL methods and higher translation
performance compared to fine-tuning a pre-trained language model (PLM),
specifically mBART-50.
