# LLM4Decompile: Decompiling Binary Code with Large Language Models

arXiv: https://arxiv.org/abs/2403.05286 · HTML: https://mlsanity.com/paper/2403.05286/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 61 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [DOBF: A Deobfuscation Pre-Training Objective for Programming Languages](https://mlsanity.com/paper/2102.07492/) (2021)
- [DIRE: A Neural Approach to Decompiled Identifier Naming](https://mlsanity.com/paper/1909.09029/) (2019)
- [Towards Neural Decompilation](https://mlsanity.com/paper/1905.08325/) (2019)
- [Modeling Black-Box Components with Probabilistic Synthesis](https://mlsanity.com/paper/2010.04811/) (2020)

## Built on by (most cited first, 30 of 39)

- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [ProgramBench: Can Language Models Rebuild Programs From Scratch?](https://mlsanity.com/paper/2605.03546/) (2026)
- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [Assemblage: Automatic Binary Dataset Construction for Machine Learning](https://mlsanity.com/paper/2405.03991/) (2024)
- [AIRTBench: Measuring Autonomous AI Red Teaming Capabilities in Language Models](https://mlsanity.com/paper/2506.14682/) (2025)
- [MALSIGHT: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarization](https://mlsanity.com/paper/2406.18379/) (2024)
- [EditLord: Learning Code Transformation Rules for Code Editing](https://mlsanity.com/paper/2504.15284/) (2025)
- [DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios](https://mlsanity.com/paper/2505.11340/) (2025)
- [HELIOS: Hierarchical Graph Abstraction for Structure-Aware LLM Decompilation](https://mlsanity.com/paper/2601.14598/) (2026)
- [VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction](https://mlsanity.com/paper/2408.07181/) (2024)
- [Deconstructing Obfuscation: A four-dimensional framework for evaluating Large Language Models assembly code deobfuscation capabilities](https://mlsanity.com/paper/2505.19887/) (2025)
- [A Decompilation-Driven Framework for Malware Detection with Large Language Models](https://mlsanity.com/paper/2601.09035/) (2026)
- [Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!](https://mlsanity.com/paper/2412.12163/) (2024)
- [Can Large Language Models Understand Intermediate Representations in Compilers?](https://mlsanity.com/paper/2502.06854/) (2025)
- [REx86: A Local Large Language Model for Assisting in x86 Assembly Reverse Engineering](https://mlsanity.com/paper/2510.20975/) (2025)
- [Context-Guided Decompilation: A Step Towards Re-executability](https://mlsanity.com/paper/2511.01763/) (2025)
- [Challenges and Future Directions in Agentic Reverse Engineering Systems](https://mlsanity.com/paper/2604.14317/) (2026)
- [Empowering Autonomous Debugging Agents with Efficient Dynamic Analysis](https://mlsanity.com/paper/2604.24212/) (2026)
- [Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees](https://mlsanity.com/paper/2506.14606/) (2025)
- [Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity](https://mlsanity.com/paper/2509.23449/) (2025)
- [QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Code](https://mlsanity.com/paper/2511.01183/) (2025)
- [Exploring the Feasibility of End-to-End Large Language Model as a Compiler](https://mlsanity.com/paper/2511.04132/) (2025)
- [Asm2SrcEval: Evaluating Large Language Models for Assembly-to-Source Code Translation](https://mlsanity.com/paper/2512.00134/) (2025)
- [Exposing Hidden Interfaces: LLM-Guided Type Inference for Reverse Engineering macOS Private Frameworks](https://mlsanity.com/paper/2601.01673/) (2026)
- [The New Compiler Stack: A Survey on the Synergy of LLMs and Compilers](https://mlsanity.com/paper/2601.02045/) (2026)
- [SemRep: Generative Code Representation Learning with Code Transformations](https://mlsanity.com/paper/2603.13640/) (2026)
- [PCodeTrans: Translate Decompiled Pseudocode to Compilable and Executable Equivalent](https://mlsanity.com/paper/2603.14855/) (2026)
- [LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering](https://mlsanity.com/paper/2604.06095/) (2026)
- [Agentic Vulnerability Reasoning on COTS Binaries](https://mlsanity.com/paper/2605.05000/) (2026)
- [ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage](https://mlsanity.com/paper/2605.21615/) (2026)

## Abstract (arXiv)

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. Motivated by the advancements in Large Language Models (LLMs), we propose LLM4Decompile, the first and largest open-source LLM series (1.3B to 33B) trained to decompile binary code. We optimize the LLM training process and introduce the LLM4Decompile-End models to decompile binary directly. The resulting models significantly outperform GPT-4o and Ghidra on the HumanEval and ExeBench benchmarks by over 100% in terms of re-executability rate. Additionally, we improve the standard refinement approach to fine-tune the LLM4Decompile-Ref models, enabling them to effectively refine the decompiled code from Ghidra and achieve a further 16.2% improvement over the LLM4Decompile-End. LLM4Decompile demonstrates the potential of LLMs to revolutionize binary code decompilation, delivering remarkable improvements in readability and executability while complementing conventional tools for optimal results. Our code, dataset, and models are released at https://github.com/albertan017/LLM4Decompile