小模型本地运行可高效分类安全事件，温度影响不大
Temperature in SLMs: Impact on Incident Categorization in On-Premises Environments
- 用1亿到20亿参数的本地小模型处理安全事件分类
- 温度调参对准确率影响小，模型大小和显卡性能决定效果
- 适合注重数据隐私的本地化安全团队使用
安全运营中心（SOCs）和计算机应急响应团队（CSIRTs）面临自动化安全事件分类的压力，但云上大语言模型存在成本高、延迟大和保密性风险。本文研究本地部署的小规模语言模型（SLMs）能否胜任该任务。我们评估了21个参数量从10亿到200亿不等的模型，在两种不同架构下调整温度超参数，测量执行时间与分类精度。结果表明，温度对性能影响甚微，而模型参数量和GPU算力是决定性因素。
原文摘要 · Abstract (English)
SOCs and CSIRTs face increasing pressure to automate incident categorization, yet the use of cloud-based LLMs introduces costs, latency, and confidentiality risks. We investigate whether locally executed SLMs can meet this challenge. We evaluated 21 models ranging from 1B to 20B parameters, varying the temperature hyperparameter and measuring execution time and precision across two distinct architectures. The results indicate that temperature has little influence on performance, whereas the number of parameters and GPU capacity are decisive factors.
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