Summary of Ladder: a Model-agnostic Framework Boosting Llm-based Machine Translation to the Next Level, by Zhaopeng Feng et al.
Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level
by Zhaopeng Feng, Ruizhe Chen, Yan Zhang, Zijie Meng, Zuozhu Liu
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 In this paper, the authors present MT-Ladder, a novel tool that refines the performance of general-purpose Large Language Models (LLMs) for machine translation. The approach is model-agnostic and cost-effective, requiring only pseudo-refinement triplets obtained from existing LLMs without additional human effort. The authors propose a hierarchical fine-tuning strategy with an easy-to-hard schema to improve MT-Ladder’s refining performance progressively. By integrating MT-Ladder with general-purpose LLMs, the translation performance can be significantly boosted. For example, using Gemma-2B/7B as the backbone, MT-Ladder-2B elevates raw translations to the level of top-tier open-source models, while MT-Ladder-7B achieves state-of-the-art performance comparable to GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MT-Ladder is a new tool that helps general-purpose language models translate better. It takes existing models and makes them even more accurate without needing much human work or data. The authors developed a special way of fine-tuning the model, which gets easier and harder in steps, to make it better at refining translations. This means you can use MT-Ladder with any general-purpose model to get better results. For instance, combining MT-Ladder-2B with Gemma-2B/7B makes the translations as good as top models, while MT-Ladder-7B performs just like GPT-4. |
Keywords
* Artificial intelligence * Fine tuning * Gpt * Translation
