CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision

• URL: http://arxiv.org/abs/2402.16928v1
• Date: Mon, 26 Feb 2024 13:49:52 GMT
• Title: CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision
• Authors: Hao Wang, Zeyu Gao, Chao Zhang, Zihan Sha, Mingyang Sun, Yuchen Zhou, Wenyu Zhu, Wenju Sun, Han Qiu, Xi Xiao
• Abstract summary: We present CLAP (Contrastive Language-Assembly Pre-training), which employs natural language supervision to learn better representations of binary code. At the core, our approach boosts superior transfer learning capabilities by effectively aligning binary code with their semantics explanations. We have generated 195 million pairs of binary code and explanations and trained a prototype of CLAP.
• Score: 22.42846252594693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Binary code representation learning has shown significant performance in binary analysis tasks. But existing solutions often have poor transferability, particularly in few-shot and zero-shot scenarios where few or no training samples are available for the tasks. To address this problem, we present CLAP (Contrastive Language-Assembly Pre-training), which employs natural language supervision to learn better representations of binary code (i.e., assembly code) and get better transferability. At the core, our approach boosts superior transfer learning capabilities by effectively aligning binary code with their semantics explanations (in natural language), resulting a model able to generate better embeddings for binary code. To enable this alignment training, we then propose an efficient dataset engine that could automatically generate a large and diverse dataset comprising of binary code and corresponding natural language explanations. We have generated 195 million pairs of binary code and explanations and trained a prototype of CLAP. The evaluations of CLAP across various downstream tasks in binary analysis all demonstrate exceptional performance. Notably, without any task-specific training, CLAP is often competitive with a fully supervised baseline, showing excellent transferability. We release our pre-trained model and code at https://github.com/Hustcw/CLAP.

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