{
  "added_at": "2026-10-16",
  "additional_evidence_urls": [
    "https://arxiv.org/abs/2403.05286"
  ],
  "category": "learning",
  "description_en": "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.",
  "description_zh": "研究使用语言模型直接反编译二进制，以及改进 Ghidra 输出。在 HumanEval 与 ExeBench 上评估重新执行能力，可用于区分代码可读性与功能恢复。",
  "entry_url": "https://awesomerea.org/resources/llm4decompile-paper/",
  "entry_url_zh": "https://awesomerea.org/zh/resources/llm4decompile-paper/",
  "evidence_url": "https://aclanthology.org/2024.emnlp-main.203/",
  "group": "learn",
  "id": "llm4decompile-paper",
  "json_url": "https://awesomerea.org/resources/llm4decompile-paper/record.json",
  "kind": "research",
  "markdown_url": "https://awesomerea.org/resources/llm4decompile-paper/index.md",
  "markdown_url_zh": "https://awesomerea.org/zh/resources/llm4decompile-paper/index.md",
  "name": "LLM4Decompile (EMNLP 2024)",
  "name_zh": "LLM4Decompile（EMNLP 2024）",
  "record_type": "resource",
  "relationship": "learning",
  "requirements": [
    "EMNLP 2024 paper. Results are author-reported for the evaluated models and benchmarks."
  ],
  "requirements_zh": [
    "EMNLP 2024 论文。结论来自作者在特定模型与基准上的实验。"
  ],
  "targets": [
    "binary"
  ],
  "url": "https://aclanthology.org/2024.emnlp-main.203/",
  "verification": "source-reviewed",
  "verified_at": "2026-10-16"
}