asmsense 数据集：22.8 万条去符号 x86-64 函数及 Qwen3-8B 模型
AI 总结
开发者以 /u/luxurycashew 的身份在 r/MachineLearning 发布 asmsense，这是一个面向去符号二进制函数命名与描述的数据集，配套一个基于 Qwen3-8B 加 LoRA 的小模型。数据集目前包含来自 341 个项目的 228,177 个函数，以 -O0 至 -Os 编译，提供英语和土耳其语描述，中文描述尚在计划中。作者自行编译宽松许可的 C 项目，使每个函数保留真实名称与源码，并公开列出仍缺的部分：ARM64、RISC-V、MIPS 等其他架构，GCC/ELF 构建（当前均为 macOS 上的 clang/Mach-O），人工核验标签，以及 BinMetric、REBench、BinSum、DeBinVul 等公开基准上的评测。
深度见解
作者说明描述由能看到 C 源码的教师模型生成、仅由另一个模型抽检，这意味着该数据集的标签质量尚未经过人工验证。
| asmsense is a dataset and a small model (Qwen3-8B + LoRA) for naming and describing functions in stripped binaries. I compiled permissively licensed C projects myself, so every function has its real name and source. Right now it has 228,177 functions from 341 projects, compiled at -O0 through -Os, with English and Turkish descriptions (Chinese is planned). What’s missing is listed step by step on one page: https://asmsense.luxurycashew.com/data/ Code and data: https://github.com/krxi/asmsense In short: other architectures (ARM64, RISC-V, MIPS), GCC/ELF builds (so far everything is clang/Mach-O on macOS), Chinese descriptions, human-checked labels, agent trajectories, and runs on public benchmarks like BinMetric, REBench, BinSum and DeBinVul. I’m thinking about turning the pipeline into an RL environment: compile, strip, let the model name the function, and score it against the real name with token-level F1. That reward is easy to game, though: a model can collect partial credit with generic names like get_data. If you’ve trained on a fuzzy target like this, how did you shape the reward, and did RL beat plain fine-tuning? Two other questions: A note on LLM use: the descriptions were written by teacher LLMs that could see the C source, and only a sample was checked, by another LLM. I also used LLMs while building this and checking my writings to check grammar, so there may be small mistakes; corrections are welcome. https://preview.redd.it/bplp468vr7wh1.png?width=1822&format=png&auto=webp&s=1b39946680aa8e3df218ccb688551c658b92f741 submitted by /u/luxurycashew [link] [comments] |