用C代码提升大模型对汇编的理解能力
C-ing Clearly: Enhanced Binary Code Explanations using C code
- 通过对应C代码生成合成数据，增强模型对汇编的认知
- 在二进制代码摘要和漏洞检测任务上表现显著提升
- 适用于不同模型家族与规模，通用性强
大型语言模型（LLMs）在高级编程语言任务中表现优异，但在低级语言如汇编方面表现较弱。我们提出一种名为C-ing Clearly的合成数据生成方法，利用对应的C代码来增强大模型对汇编的理解。通过对该方法生成的数据进行微调，我们在二进制代码摘要和漏洞检测任务上均取得了性能提升。该方法在不同类型的大型语言模型及模型规模下均表现出一致的改进效果。
原文摘要 · Abstract (English)
Large Language Models (LLMs) typically excel at coding tasks involving high-level programming languages, as opposed to lower-level programming languages, such as assembly. We propose a synthetic data generation method named C-ing Clearly, which leverages the corresponding C code to enhance an LLM's understanding of assembly. By fine-tuning on data generated through our method, we demonstrate improved LLM performance for binary code summarization and vulnerability detection. Our approach demonstrates consistent gains across different LLM families and model sizes.
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