Summary of General2specialized Llms Translation For E-commerce, by Kaidi Chen et al.
General2Specialized LLMs Translation for E-commerce
by Kaidi Chen, Ben Chen, Dehong Gao, Huangyu Dai, Wen Jiang, Wei Ning, Shanqing Yu, Libin Yang, Xiaoyan Cai
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the issue of Neural Machine Translation (NMT) models struggling to translate texts from domains like e-commerce and legal documents. These domains have unique writing styles that current NMT methods are not equipped to handle. To overcome this limitation, the authors collect domain-specific resources, including term pairs and a parallel corpus, and propose a two-step fine-tuning paradigm called G2ST. This approach enables the transfer of general NMT models to specialized models for e-commerce tasks. The G2ST method is demonstrated to outperform state-of-the-art NMT models like LLaMA, Qwen, GPT-3.5, and even GPT-4 on real e-commerce title translations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps solve a problem with machine translation. Right now, machines are not very good at translating texts from specific areas like online shopping or laws. They struggle because these areas have their own special writing styles that machines don’t understand well. To fix this issue, the researchers collected some special tools and created a new way to fine-tune machine translation models for e-commerce tasks. This approach was tested on real-life examples and showed that it’s much better than existing methods. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Llama » Translation
