Summary of Generating Unseen Code Tests in Infinitum, by Marcel Zalmanovici and Orna Raz and Eitan Farchi and Iftach Freund
Generating Unseen Code Tests In Infinitum
by Marcel Zalmanovici, Orna Raz, Eitan Farchi, Iftach Freund
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method creates benchmark variations that generalize across coding tasks and programming languages, addressing the issue of training data leakage in Large Language Models (LLMs). The approach enables ongoing test-data generation, mitigating the problem. A specific benchmark, called auto-regression, is implemented for text-to-code generation in Python, aiding debugging and tracking model changes during regression testing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are used for many tasks, including coding-related activities. To assess their fitness, benchmarks are commonly used. However, these benchmarks suffer from the issue of training data leakage. A new method is presented to create benchmark variations that can be applied across different coding tasks and programming languages. This approach helps generate ongoing test-data, solving the problem of training data leakage. |
Keywords
» Artificial intelligence » Regression » Tracking
