Summary of An Empirical Study Of Validating Synthetic Data For Formula Generation, by Usneek Singh et al.
An Empirical Study of Validating Synthetic Data for Formula Generation
by Usneek Singh, José Cambronero, Sumit Gulwani, Aditya Kanade, Anirudh Khatry, Vu Le, Mukul Singh, Gust Verbruggen
First submitted to arxiv on: 15 Jul 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 In this paper, researchers explore ways to improve large language models’ ability to write formulas in spreadsheets. They focus on a specific challenge: validating synthetic training examples generated by another model to ensure they are accurate and helpful for fine-tuning the original model. The authors demonstrate that using surrogate objectives to evaluate the accuracy of these synthetic annotations can significantly improve performance across four different models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can help write formulas in spreadsheets, but we need better resources to make them work well. The problem is that there’s not much information available about these formulas, which holds back how well pre-trained models perform and limits their ability to learn new things. One way to fix this is to use another model to create fake text examples based on a collection of formulas. But before we can fine-tune the original model using these fake texts, we need to make sure they’re accurate. In this study, researchers show that checking if these fake texts are correct makes a big difference in how well the models perform. |
Keywords
* Artificial intelligence * Fine tuning
