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Impact of Code Length on LLMs in Vulnerability Detection

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🔍 Study Reveals Impact of Code Length on LLM Performance in Vulnerability Detection. This research evaluates how the length of tokenized Java code affects the accuracy of ten prominent large language models (LLMs) in detecting vulnerabilities. The findings indicate that models like GPT-4, Mistral, and Mixtral demonstrate robustness, while others show a significant correlation between token length and performance. The study suggests that future LLM development should aim to reduce the impact of input length on detection capabilities. Additionally, implementing preprocessing techniques to decrease token count while maintaining code structure could improve the accuracy and clarity of LLMs in vulnerability detection tasks.

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