Summary of Scaling Laws For Downstream Task Performance in Machine Translation, by Berivan Isik et al.
Scaling Laws for Downstream Task Performance in Machine Translation
by Berivan Isik, Natalia Ponomareva, Hussein Hazimeh, Dimitris Paparas, Sergei Vassilvitskii, Sanmi Koyejo
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 The study explores scaling laws in transfer learning settings for large language models (LLMs) finetuned for machine translation tasks. Researchers investigate how the choice of pretraining data and its size affect downstream performance metrics such as cross-entropy, BLEU, and COMET scores. They find that sufficient distribution alignment between pretraining and downstream data leads to improved translation quality with increasing pretraining data, which can be predicted using a log-law. However, moderate misalignment can cause fluctuations or worsen translation scores while improving cross-entropy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting better at understanding human languages. Researchers looked at how these models perform when they’re “finetuned” for specific tasks like translating text from one language to another. They wanted to know what happens when you give these models more data to train on, and if there’s a way to predict how well they’ll do. The results show that if the model is trained on similar types of texts before being used for translation, it gets better at translating as it receives more training. But if the training data is very different from what the model is actually supposed to translate, it can get worse or stay the same even with more training. |
Keywords
* Artificial intelligence * Alignment * Bleu * Cross entropy * Pretraining * Scaling laws * Transfer learning * Translation
