## 2 Related Work
### NLP-Based Methods  
Recent research has regarded code as natural language and leveraged natural language models to extract code representations for detecting software vulnerabilities. [PERSON] et al. [13] developed Asm2 vec, which employs PV-DM [14] to generate function embeddings specifically for vulnerability detection. This approach, however, is limited to a single instruction set architecture, constraining its application in cross-architecture vulnerability detection. To overcome this limitation, [PERSON] et al. [7] introduced SAFE, which is a self-attention-based neural network model that generates instruction embeddings using word2 vec [15]. These embeddings are further processed by a bi-directional recurrent neural network (RNN) with an attention mechanism used to derive function embeddings. However, this method is only feasible across ARM and AMD architectures. To address such issues, [PERSON] et al. [4] proposed an innovative multi-architecture instruction embedding approach using Unsupervised Multilingual Word Embeddings (UMWE) [16]. This method maps codes from diverse architectures into a shared semantic space, enabling the extraction of common semantic information across ARM, MIPS, and X86 architectures. The Transformer model [6], a neural network based on self-attention mechanisms, can globally model each token in a token sequence and has achieved notable success in NLP. Recently, researches have tried to apply the Transformer model to extract semantics from binary code for vulnerability detection. [PERSON] et al. [17] developed an adaptive binary code similarity analysis method for vulnerability discovery. This system integrates semantic and structural features of functions and utilizes the Transformer's attention mechanism to maintain robustness across different compilation settings. BERT [12], recognized for its high-performance capabilities as a Transformer method with efficient semantic extraction, is extensively employed by researchers in vulnerability detection methods. For example,Yu et al. [18] developed pre-training tasks at both the basic block and function levels to train a BERT model, generating semantic embeddings for function basic blocks. Subsequently, they utilized the Control Flow Graph (CFG) of a function and a Message Passing Neural Network (MPNN) [19] to learn the structural features of functions and integrated these with sequence features to create comprehensive function embeddings. [PERSON] et al. [2] utilized an intermediate language to mitigate the effects of architectural differences on code homogeneity assessments and introduced the novel Intermediate Representation Function Model (IRFM). This method transforms binary code into microcode [20] and establishes root operand prediction and adjacent block prediction tasks for training the RoBerta model [21]. This training enables the model to understand the relationships between operands and the data flow within basic blocks, facilitating the generation of semantic embeddings for basic blocks. Finally, a Graph Convolutional Network (GCN) [22] is employed to create function embeddings. [PERSON] et al. [23] developed BinAIV, which is a method that utilizes SimBERT [24] to learn the representation of assembly code and incorporates function name information into function semantics, thus enhancing the accuracy of vulnerability detection. Concurrently, [PERSON] et al. [25] utilize BERT to extract semantic information from instructions and employ a graph encoder with a self-gating layer to capture both structural and semantic features within CFGs. Large language models (LLMs) [26], known for their powerful analytical and reasoning capabilities, are increasingly employed to improve performance in vulnerability detection. [PERSON] et al. [27] enhanced the capabilities of LLMs for vulnerability detection by integrating structural information with in-context learning [28]. This approach evaluates semantic, lexical, and syntactic similarities to accurately identify code examples that are most similar to the target code.