Summary of Towards a Theoretical Understanding Of Synthetic Data in Llm Post-training: a Reverse-bottleneck Perspective, by Zeyu Gan et al.
Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective
by Zeyu Gan, Yong Liu
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 A novel theoretical foundation for synthetic data generation in large language models (LLMs) is presented, focusing on the connection between generalization capability and information gain. A detailed modeling of the synthetic data generation process is introduced, followed by an analysis of how generalization capability is critically determined by the information gain derived from the generative model. The concept of Generalization Gain via Mutual Information (GGMI) is also introduced, highlighting its relationship with information gain. This theoretical foundation serves as a starting point for designing synthetic data generation techniques and optimizing the post-training process. The paper’s findings are demonstrated through experiments and open-sourced code is available at this https URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Synthetic data helps big language models learn better. Researchers have been trying to make fake data that can help these models improve. But there’s a problem – we don’t fully understand how well the fake data works. To fix this, scientists created a detailed model of how the fake data is made and showed that how good the fake data is affects how well the language model learns. They also came up with a new way to measure how well the fake data helps the language model learn. This new understanding can help us make better fake data and improve how well language models work. |
Keywords
» Artificial intelligence » Generalization » Generative model » Language model » Synthetic data
