Llama 2 avoids errors by staying quiet, GPT-4 gives long, if useless, samples
Discover how Llama 2 outperforms GPT-4 in generating reliable code, revealing crucial insights on the effectiveness of large language models.
The article discusses a study conducted by computer scientists at the University of California San Diego on the reliability and robustness of large language models (LLMs) in generating code. The researchers evaluated four different code-capable LLMs using an API checker called RobustAPI. They gathered 1,208 coding questions from StackOverflow involving 24 common Java APIs and tested the LLMs with three different types of questions. The results showed that the LLMs had high rates of API misuse, with GPT-3.5 and GPT-4 from OpenAI exhibiting the highest failure rates. However, Meta’s Llama 2 performed exceptionally well, with a failure rate of less than one percent. The study highlights the importance of assessing code reliability and the need for improvement in large language models’ ability to generate clean code.
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