POMP : Probability-driven Meta-graph Prompter for LLM s in Low-resource Unsupervised Neural Machine Translation (2024)

Abstract: Low-resource languages (LRLs) face challenges in supervised neural machine translation (NMT) due to limited parallel data, prompting research in unsupervised NMT.Unsupervised NMT (UNMT), without requiring ground truth, provides solutions for LRL translations using synthetic pseudo-parallel data and parallel data from auxiliary language pairs. However, they usually encounter translation errors, including errors from synthetic data and from auxiliary language pairs with linguistic biases.We argue that large language models (LLMs) mitigate UNMT’s translation errors by dynamically organizing auxiliary languages in prompts to improve LRL translations. In this paper, we propose \textbf{P} r \textbf{O} bability-driven \textbf{M} eta-graph \textbf{P} rompter (POMP), an approach employing a dynamic graph to organize multiple auxiliary languages, to prompt LLMs in LRL translations. POMP proposes a language-specific meta-graph that dynamically samples multiple translation paths to organize auxiliary languages in constructing prompts. Following the path, POMP prompts LLMs to translate with a mixture of auxiliary languages. We achieve the meta-graph’s evolution by back-propagating evaluation scores to update probabilities on the graph.Our experimental improvements show POMP’s effectiveness on LRLs’ translation.

## introduction training supervised nmt models requires extensive parallel data @xcite , which struggles in low-resource languages (lrls) that have limited parallel data. researchers extract @xcite and annotate @xcite parallel samples of lrls, which requires a substantial human effort. therefore, the research community explores unsupervised methods to achieve nmt without parallel data. ## conclusion in summary, we propose pomp, an unsupervised method that mitigates linguistically biased translation errors in unmt on lrls. this approach involves constructing a language-specific metagraph, from which we sample multiple translation paths with organized auxiliary languages to prompt llms as lrl translators. we promote the evolution of the meta-graph by back-propagating evaluation scores to update probabilities of involved auxiliary languages in the graph. we use three metrics for evaluations in testing. our experiment results demonstrate the effectiveness of our approach, which achieves sota performances on four lrls.