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Large Language Models for Code Analysis: Do LLMs Really Do Their Job?
March 7, 2024, 5:11 a.m. | Chongzhou Fang, Ning Miao, Shaurya Srivastav, Jialin Liu, Ruoyu Zhang, Ruijie Fang, Asmita, Ryan Tsang, Najmeh Nazari, Han Wang, Houman Homayoun
cs.CR updates on arXiv.org arxiv.org
Abstract: Large language models (LLMs) have demonstrated significant potential in the realm of natural language understanding and programming code processing tasks. Their capacity to comprehend and generate human-like code has spurred research into harnessing LLMs for code analysis purposes. However, the existing body of literature falls short in delivering a systematic evaluation and assessment of LLMs' effectiveness in code analysis, particularly in the context of obfuscated code.
This paper seeks to bridge this gap by offering …
analysis arxiv body code code analysis cs.cr cs.se human job language language models large llms natural natural language programming realm research understanding
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