Papers with Decompilation
LLM4Decompile: Decompiling Binary Code with Large Language Models (2024.emnlp-main)
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| Challenge: | Decompilation aims to convert binary code to high-level source code, but traditional tools like Ghidra often produce results that are difficult to read and execute. |
| Approach: | They propose an open-source LLM series trained to decompile binary code . they optimize the LLM training process and introduce the Llm4Decompile-End models . |
| Outcome: | The proposed models outperform GPT-4o and Ghidra on the HumanEval and ExeBench benchmarks by over 100% in terms of re-executability rate. |
Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement (2024.findings-emnlp)
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| Challenge: | Decompilation is the process of converting compiled code back into a high-level programming language for analysis when source code is unavailable. |
| Approach: | They propose two methods to improve decompilation performance without fine-tuning and fine-grained alignment enhancement to achieve further improvements. |
| Outcome: | The proposed methods achieved a Re-Executability performance improvement of approximately 3.90% on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 52.41%. |