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; the first arXiv submission date is listed separately.
Sources reviewed
Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.
Related entries
Matched by the same code repository or publication identifier.
- LLM4Decompile (EMNLP 2024) Curated resource
Discussed in the survey
Primary sources
- https://aclanthology.org/2024.emnlp-main.203/
- https://aclanthology.org/2024.emnlp-main.203.pdf
- https://arxiv.org/abs/2403.05286
- https://github.com/albertan017/LLM4Decompile
- README.md GitHub · albertan017/LLM4Decompile · Revision 85b364b