Summary of Fine-tuning Large Language Models For Domain-specific Machine Translation, by Jiawei Zheng et al.
Fine-tuning Large Language Models for Domain-specific Machine Translation
by Jiawei Zheng, Hanghai Hong, Feiyan Liu, Xiaoli Wang, Jingsong Su, Yonggui Liang, Shikai Wu
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 proposes a novel fine-tuning framework, DragFT, to enhance the domain-specific machine translation capabilities of large language models (LLMs). DragFT augments LLMs with three techniques: dictionary-enhanced prompting, RAG-based few-shot example selection, and fine-tuning with few-shot examples. The authors deploy DragFT on three well-known LLM backbones and evaluate its performance on three domain-specific datasets. The results show that DragFT achieves a significant performance boost and outperforms advanced models like GPT-3.5 and GPT-4o. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models become better at translating text from one specific area of expertise to another. For example, it can help translate medical texts from English to Spanish or engineering texts from French to German. The authors developed a new way to train these models using special datasets and techniques that make the training process more effective. |
Keywords
* Artificial intelligence * Few shot * Fine tuning * Gpt * Prompting * Rag * Translation
