Summary of Cookbook: a Framework For Improving Llm Generative Abilities Via Programmatic Data Generating Templates, by Avanika Narayan et al.
Cookbook: A framework for improving LLM generative abilities via programmatic data generating templates
by Avanika Narayan, Mayee F. Chen, Kush Bhatia, Christopher Ré
First submitted to arxiv on: 7 Oct 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 Medium Difficulty summary: Fine-tuning large language models (LLMs) on instruction datasets is a common approach to improve their generative capabilities. However, manually curating these datasets can be time-consuming and expensive, while LLM-generated data may violate user privacy agreements or terms of service. To address this issue, we introduce Cookbook, a framework that programmatically generates training data using simple patterns over random tokens. This scalable, cost-effective approach avoids legal and privacy issues. We find that fine-tuning on Cookbook-generated data can improve performance by up to 52.7 accuracy points. Additionally, Cookbook algorithmically learns how to mix data from various templates to optimize performance on multiple tasks, outperforming other models in the standard GPT4ALL evaluation suite. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers are working on ways to make computers better at understanding and generating human language. One way they do this is by giving large language models (LLMs) special training data called instruction datasets. But making these datasets can be difficult and time-consuming, and using computer-generated data might not follow the rules or respect people’s privacy. To solve this problem, scientists created a new tool called Cookbook that helps make training data more quickly and easily. This tool uses simple patterns to generate lots of data, which can help LLMs get better at understanding and generating language. |
Keywords
* Artificial intelligence * Fine tuning
