ParaVul: A Parallel Large Language Model and Retrieval-Augmented Framework for Smart Contract Vulnerability Detection

Summary

ParaVul is a framework that combines parallel LLM fine-tuning with retrieval-augmented generation to detect smart contract vulnerabilities more accurately than static analysis and formal verification. It introduces Sparse Low-Rank Adaptation (SLoRA), which inserts parallel sparse and low-rank branches after the attention projection and feed-forward block, and a hybrid RAG system combining Okapi BM25 with dense retrieval. Simulation results report F1 scores of 0.9398 for single-label and 0.9930 for multi-label detection.

Topics

• Retrieval-augmented generation