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Recent studies reveal that large language models (LLMs) frequently generate fictitious software package names, raising significant concerns. This phenomenon, termed 'hallucination,' poses risks as developers may unknowingly incorporate malicious packages into their code. The research highlights the prevalence of these inaccuracies, with commercial models showing a lower rate of hallucinations compared to open-source counterparts.
The findings indicate that while LLMs can enhance coding productivity, their reliability is questionable, especially in critical applications. Researchers found that the average hallucination rate for commercial models is 5.2%, while open-source models reach 21.7%. This underscores the need for caution when using AI-generated suggestions in software development.
• LLMs often generate fictitious software package names, posing security risks.
• Commercial models show lower hallucination rates than open-source models.
Hallucinations present a critical obstacle to the effective deployment of LLMs in applications.
LLMs are used for various applications, including code generation, but can produce unreliable outputs.
RAG was used to mitigate hallucinations in AI-generated code.
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