Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.10699 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913970234851328 |
|---|---|
| author | Na, Hongbin Wang, Zimu Maimaiti, Mieradilijiang Chen, Tong Wang, Wei Shen, Tao Chen, Ling |
| author_facet | Na, Hongbin Wang, Zimu Maimaiti, Mieradilijiang Chen, Tong Wang, Wei Shen, Tao Chen, Ling |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10699 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Thinker-DDM: Modeling Deliberation for Machine Translation with a Drift-Diffusion Process Na, Hongbin Wang, Zimu Maimaiti, Mieradilijiang Chen, Tong Wang, Wei Shen, Tao Chen, Ling Computation and Language 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. |
| title | Thinker-DDM: Modeling Deliberation for Machine Translation with a Drift-Diffusion Process |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.10699 |