Summary of Training Data For Large Language Model, by Yiming Ju et al.
Training Data for Large Language Model
by Yiming Ju, Huanhuan Ma
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 abstract discusses the significance of high-quality datasets in developing powerful language models. The ChatGPT model’s impressive performance improvements through fine-tuning on large annotated datasets have led to a focus on dataset construction and optimization in AI research. The paper provides an overview of current methods for pretraining and fine-tuning data, including data scale, collection methods, types, characteristics, and processing workflows, as well as highlights available open-source datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large-scale language models like ChatGPT have made a big impact! Researchers are now focusing on creating better datasets to make these models even smarter. The paper looks at what makes a good dataset for training these powerful models. It covers things like how much data you need, how you collect it, and what kind of data is best. |
Keywords
» Artificial intelligence » Fine tuning » Optimization » Pretraining
