File size: 2,489 Bytes
d8ece85 2a4a8ac f27079c 2a4a8ac 5b5aaa9 2a4a8ac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
---
license: mit
datasets:
- ylfeng/sccdec-dataset
language:
- en
base_model:
- LLM4Binary/llm4decompile-6.7b-v1.5
---
# Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement
Model For [Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement](https://arxiv.org/abs/2406.17233).
+ [Code in Github](https://github.com/AlongWY/sccdec)
+ [Model in huggingface](https://huggingface.co/ylfeng/sccdec-lora).
+ [Dataset in huggingface](https://huggingface.co/datasets/ylfeng/sccdec-dataset).
If you use SCCDEC in your work, please cite it as follows:
```
@inproceedings{feng-etal-2024-self,
title = "Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement",
author = "Feng, Yunlong and
Teng, Dechuan and
Xu, Yang and
Mu, Honglin and
Xu, Xiao and
Qin, Libo and
Zhu, Qingfu and
Che, Wanxiang",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.385",
pages = "6603--6614",
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 during the fine-tuning phase to achieve further improvements in decompilation. By integrating these two methods, we achieved a Re-Executability performance improvement of approximately 3.90{\%} on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 52.41{\%}. The code, data, and models are available at https://github.com/AlongWY/sccdec.",
}
```
* License: MIT |