Analysis and comprehension of assembly code are crucial in various applications, such as reverse engineering. However, the low information density and lack of explicit syntactic structures in assembly code pose significant challenges. Pioneering approaches with masked language modeling (MLM)-based methods have been limited by facilitating natural language interaction. While recent methods based on decoder-focused large language models (LLMs) have significantly enhanced semantic representation, they still struggle to capture the nuanced and sparse semantics in assembly code. In this paper, we propose Assembly Augmented Tuning (ASMA-Tune), an end-to-end structural-semantic instruction-tuning framework. Our approach synergizes encoder architectures with decoder-based LLMs through projector modules to enable comprehensive code understanding. Experiments show that ASMA-Tune outperforms existing benchmarks, significantly enhancing assembly code comprehension and instruction-following abilities. Our model and dataset are public at https://github.com/wxy3596/ASMA-Tune.

本研究针对汇编代码分析与理解面临的低信息密度和缺乏明确语法结构的问题，提出了一种新的解决方法ASMA-Tune，结合编码器架构与基于解码器的大型语言模型进行结构-语义指令调优。实验结果表明，ASMA-Tune在汇编代码理解和指令跟随能力上超越了现有基准，展示了其显著的性能提升与潜在影响。

ASMA-Tune：通过结构-语义指令调优解锁LLM的汇编代码理解能力