Large language models (LLMs) have demonstrated promising potential in various
downstream tasks, including machine translation. However, prior work on
LLM-based machine translation has mainly focused on better utilizing training
data, demonstrations, or pre-defined and universal knowledge to improve
performance, with a lack of consideration of decision-making like human
translators. In this paper, we incorporate Thinker with the Drift-Diffusion
Model (Thinker-DDM) to address this issue. We then redefine the Drift-Diffusion
process to emulate human translators' dynamic decision-making under constrained
resources. We conduct extensive experiments under the high-resource,
low-resource, and commonsense translation settings using the WMT22 and CommonMT
datasets, in which Thinker-DDM outperforms baselines in the first two
scenarios. We also perform additional analysis and evaluation on commonsense
translation to illustrate the high effectiveness and efficacy of the proposed
method.

利用 Thinker 和 Drift-Diffusion 模型重新定义漂移扩散过程来模拟人类翻译者在受限资源下的动态决策制定，通过对 WMT22 和 CommonMT 数据集进行广泛实验，发现 Thinker-DDM 在高资源和低资源翻译设置中表现优于基线模型，并对常识翻译进行额外分析和评估，证明了所提出方法的高效性和有效性。