Rethinking Human-like Translation Strategy: Integrating Drift-Diffusion Model with Large Language Models for Machine Translation

arXiv:2402.10699v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.cl data diffusion diffusion model drift human human-like knowledge language language models large language large language models llm llms machine machine translation performance prior strategy tasks training training data translation type work