Summary of Target-aware Language Modeling Via Granular Data Sampling, by Ernie Chang et al.
Target-Aware Language Modeling via Granular Data Sampling
by Ernie Chang, Pin-Jie Lin, Yang Li, Changsheng Zhao, Daeil Kim, Rastislav Rabatin, Zechun Liu, Yangyang Shi, Vikas Chandra
First submitted to arxiv on: 23 Sep 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 introduces a cost-effective approach to pretraining language models that excel in specific areas without compromising performance on other tasks. The authors revisit importance sampling with n-gram features, which balances sentence compression and representation capabilities. They demonstrate the effectiveness of this paradigm by training models on selected documents, achieving comparable or better performance compared to full RefinedWeb data, using only 1% of the total data. This approach is particularly useful for large-scale pretraining data selection for domain-specific use cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to train language models that are good at one specific thing without being bad at other things. Right now, most language model training tries to be good at lots of different things at once. But what if we could focus on just one area and still get good results? The authors found a way to do this by using special features called n-gram tokens that help the model understand sentences better. They showed that models trained with this approach can perform as well or even better than models trained on all the data, using only a small fraction of it. |
Keywords
» Artificial intelligence » Language model » N gram » Pretraining
