Summary of Balancing Cost and Effectiveness Of Synthetic Data Generation Strategies For Llms, by Yung-chieh Chan et al.
Balancing Cost and Effectiveness of Synthetic Data Generation Strategies for LLMs
by Yung-Chieh Chan, George Pu, Apaar Shanker, Parth Suresh, Penn Jenks, John Heyer, Sam Denton
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This research paper investigates the effectiveness of alternative methods for creating high-quality datasets for fine-tuning large language models (LLMs). The authors find that traditional approaches using human-generated data can be prohibitively expensive in many scenarios. Instead, they explore three categories of synthetic data generation strategies: Answer Augmentation, Question Rephrase, and New Question. They study the performance of student LLMs trained under various constraints, including seed instruction set size and query budget. The results show that the optimal data generation strategy depends strongly on the ratio between teacher query budget and seed instruction set size. When this ratio is low, generating new answers to existing questions proves most effective, but as the ratio increases, generating new questions becomes optimal. Across all tasks, the choice of augmentation method and other design choices matter substantially more in low-to-mid data regimes than high data regimes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper tries to solve a big problem. Right now, it’s hard to make really good datasets for training large language models. Most people use human-made data, but that can be too expensive or time-consuming. So, scientists are looking for new ways to make synthetic data. They tested three different methods and found out that the best one depends on how much data they already have. If they don’t have much, making new answers to old questions works well. But if they have more data, making new questions is better. This matters because it helps us understand which method to use in different situations. |
Keywords
» Artificial intelligence » Fine tuning » Synthetic data
