Summary of Humaneval on Latest Gpt Models — 2024, by Daniel Li et al.
HumanEval on Latest GPT Models – 2024
by Daniel Li, Lincoln Murr
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper leverages the advancements in large language models like GPT-4 to improve program synthesis. By connecting these models to Huamn Eval, a dataset initially designed for CODEGEN, they demonstrate competitive performance in zero-shot Python code generation on HumanEval tasks. The model’s utility is further showcased through its ability to generate more complex multi-step paradigm syntheses. This breakthrough enables the development of more sophisticated program synthesis capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses super smart computers called GPT-4 to make it easier for humans to write code. They took a special dataset and connected it to these powerful machines, which can now help us create Python code without needing any extra training. The results are really impressive and show that we can use these computers to make even more complex code generation tasks possible. |
Keywords
* Artificial intelligence * Gpt * Zero shot
