  Low-resource languages (LRLs) face challenges in supervised neural machine
translation due to limited parallel data, prompting research into unsupervised
methods. Unsupervised neural machine translation (UNMT) methods, including
back-translation, transfer learning, and pivot-based translation, offer
practical solutions for LRL translation, but they are hindered by issues like
synthetic data noise, language bias, and error propagation, which can
potentially be mitigated by Large Language Models (LLMs). LLMs have advanced
NMT with in-context learning (ICL) and supervised fine-tuning methods, but
insufficient training data results in poor performance in LRLs. We argue that
LLMs can mitigate the linguistic noise with auxiliary languages to improve
translations in LRLs. In this paper, we propose Probability-driven Meta-graph
Prompter (POMP), a novel approach employing a dynamic, sampling-based graph of
multiple auxiliary languages to enhance LLMs' translation capabilities for
LRLs. POMP involves constructing a directed acyclic meta-graph for each source
language, from which we dynamically sample multiple paths to prompt LLMs to
mitigate the linguistic noise and improve translations during training. We use
the BLEURT metric to evaluate the translations and back-propagate rewards,
estimated by scores, to update the probabilities of auxiliary languages in the
paths. Our experiments show significant improvements in the translation quality
of three LRLs, demonstrating the effectiveness of our approach.
