Summary of Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale, by Fan Zhou et al.
Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale
by Fan Zhou, Zengzhi Wang, Qian Liu, Junlong Li, Pengfei Liu
First submitted to arxiv on: 25 Sep 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 The paper introduces a novel framework called Programming Every Example (ProX) that enables small language models to refine corpora by generating and executing fine-grained operations for each individual example at scale. ProX treats data refinement as a programming task, allowing models to learn from the unique characteristics of individual examples effectively. Experimental results show that models pre-trained on ProX-curated data outperform original data or filtered data by more than 2% across various downstream benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to improve language model training using something called ProX. This framework helps small models learn from the specifics of each piece of text, which is important because big models are hard to train and use too much computing power. The results show that models trained with ProX do better than those trained without it, especially when learning about specific topics. |
Keywords
* Artificial intelligence * Language model
