Summary of Investigating the Impact Of Data Contamination Of Large Language Models in Text-to-sql Translation, by Federico Ranaldi et al.
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation
by Federico Ranaldi, Elena Sofia Ruzzetti, Dario Onorati, Leonardo Ranaldi, Cristina Giannone, Andrea Favalli, Raniero Romagnoli, Fabio Massimo Zanzotto
First submitted to arxiv on: 12 Feb 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 In this research paper, scientists investigate whether large language models can learn to generate code from natural language descriptions without prior training on programming concepts. While initial results show that these models are capable of doing so in zero-shot scenarios, the study highlights the potential for data contamination, where the model’s ability to translate text into code is influenced by having seen similar descriptions and code before. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Code generation from natural language descriptions is a significant achievement for large language models. However, researchers have found that these models may not be as good at it as initially thought. The study shows that while LLMs can generate code without prior training, this ability may be compromised if the model has seen similar text and code before. |
Keywords
* Artificial intelligence * Zero shot
