2024 · Conference paper
LLM4Decompile: Decompiling Binary Code with Large Language Models
Trains language models for assembly-to-C decompilation and for refining Ghidra pseudocode, evaluating whether recovered functions can be recompiled and executed correctly.
Scope and limitations
The paper limits its experiments to C on x86 and function-level benchmarks. Re-executability on sampled tests does not establish whole-program equivalence or recovery of the original source. Later repository releases are separate from the 2024 paper.
Why it fits this collection
Direct evidence for LLM decompilation and a concrete baseline for evaluating agent-generated or refined pseudocode.
Publication and review details
- Venue
- EMNLP 2024
- Status
- Conference paper
- Publication / posting date
- November 2024
- Date source
- ACL proceedings publication month
- First arXiv submission
- March 8, 2024
- arXiv
- 2403.05286
- Reviewed arXiv version
- v3
- DOI
- 10.18653/v1/2024.emnlp-main.203
Publication date is the conference month; first_arxiv_date is recorded separately.
Sources reviewed
Source review only; experiments were not rerun for this collection. Cite the original authors using the linked paper or BibTeX.