Summary of Simultaneous Masking, Not Prompting Optimization: a Paradigm Shift in Fine-tuning Llms For Simultaneous Translation, by Matthew Raffel et al.
Simultaneous Masking, Not Prompting Optimization: A Paradigm Shift in Fine-tuning LLMs for Simultaneous Translation
by Matthew Raffel, Victor Agostinelli, Lizhong Chen
First submitted to arxiv on: 16 May 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 A novel approach to fine-tuning large language models (LLMs) for simultaneous translation is proposed in this work. The current methods suffer from issues such as increased prompt sizes, restriction to a single decision policy, and unnecessary expansion of training sets. To address these limitations, the authors introduce SimulMask, an attention mask approach that models simultaneous translation during fine-tuning by masking attention for a desired decision policy. This method is applied to a Falcon LLM on the IWSLT 2017 dataset, resulting in significant improvements in translation quality compared to state-of-the-art prompting optimization strategies across five language pairs, while reducing computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. Right now, people are using these models for simultaneous translation, which means translating a conversation or speech in real-time. But current methods have some big problems, like making the computer work too hard or not being able to make good decisions. To fix this, researchers created a new way called SimulMask that helps the computer learn how to translate better while still being efficient. They tested it on a special kind of model and some old language translation data, and it worked really well! It was able to translate five different languages much better than other methods, and it didn’t take as long to do it. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Mask » Optimization » Prompt » Prompting » Translation
