{
 "id": "ylfeng/sccdec-dataset",
 "owner": "ylfeng",
 "name": "sccdec-dataset",
 "downloads": 55,
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 "last_modified": "2025-01-07T09:05:11.000Z",
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 "citation": "@inproceedings{feng-etal-2024-self,\n    title = \"Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement\",\n    author = \"Feng, Yunlong  and\n      Teng, Dechuan  and\n      Xu, Yang  and\n      Mu, Honglin  and\n      Xu, Xiao  and\n      Qin, Libo  and\n      Zhu, Qingfu  and\n      Che, Wanxiang\",\n    editor = \"Al-Onaizan, Yaser  and\n      Bansal, Mohit  and\n      Chen, Yun-Nung\",\n    booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2024\",\n    month = nov,\n    year = \"2024\",\n    address = \"Miami, Florida, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2024.findings-emnlp.385\",\n    pages = \"6603--6614\",\n    abstract = \"Decompilation transforms compiled code back into a high-level programming language for analysis when source code is unavailable. Previous work has primarily focused on enhancing decompilation performance by increasing the scale of model parameters or training data for pre-training. Based on the characteristics of the decompilation task, we propose two methods: (1) Without fine-tuning, the Self-Constructed Context Decompilation (sc$^2$dec) method recompiles the LLM{'}s decompilation results to construct pairs for in-context learning, helping the model improve decompilation performance. (2) Fine-grained Alignment Enhancement (FAE), which meticulously aligns assembly code with source code at the statement level by leveraging debugging information, is employed dur",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "9faf85c8f858e1bbaeb2bc78395fc11f0d349964",
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}