Research
LLM4Decompile (EMNLP 2024)
Research on direct binary decompilation and refinement of Ghidra output with language models. Evaluates re-executability on HumanEval and ExeBench; useful for distinguishing readable output from functional recovery.
Requirements and limitations
- EMNLP 2024 paper. Results are author-reported for the evaluated models and benchmarks; this collection has not reproduced them.
About this entry
- Type
- Research
- Category
- Learning resources
- Relationship
- Learning resource
- Targets
- Binary
Sources reviewed
The description was reviewed against primary sources. This does not establish runtime testing, security auditing or verified compatibility.