Summary of Rethinking Data Selection at Scale: Random Selection Is Almost All You Need, by Tingyu Xia et al.
Rethinking Data Selection at Scale: Random Selection is Almost All You Need
by Tingyu Xia, Bowen Yu, Kai Dang, An Yang, Yuan Wu, Yuan Tian, Yi Chang, Junyang Lin
First submitted to arxiv on: 12 Oct 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 Supervised fine-tuning (SFT) is crucial for aligning Large Language Models (LLMs) with human instructions. The primary goal during SFT is to select a representative subset of training data from the larger pool, ensuring fine-tuning results comparable to or even exceeding those obtained using the entire dataset. Current methods struggle to significantly outperform random selection when dealing with large-scale datasets, which highlights the importance of diversity in data selection over simply focusing on high-quality data. Our comparisons demonstrate that filtering data by token length offers a stable and efficient method for improving results, particularly when training on long text data with weaker base models like Llama3. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to teach a super smart computer how to understand what humans want it to do. This is called “fine-tuning” the computer’s language skills. The problem is that there are just too many things for the computer to learn from, so we need to pick out the most important ones. Most methods don’t work well when dealing with huge amounts of data like this. Our research shows that instead of focusing on finding only the best data, it’s more important to mix and match different types of data to help the computer learn better. We also found a simple way to improve results by filtering out certain types of data, which works especially well for computers that are still learning. |
Keywords
» Artificial intelligence » Fine tuning » Supervised » Token
