Rethinking Human-like Translation Strategy: Integrating Drift-Diffusion
  Model with Large Language Models for Machine Translation
Hongbin Na Zimu Wang Mieradilijiang Maimaiti Tong Chen Wei Wang Tao Shen Ling Chen
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.