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Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks
面向 IIoT 网络的轻量级入侵检测模型的跨域泛化失效
arXiv:2607.00553 评测基准 应用落地 OA · 绿色 被引 1 · S2

应在真实类别分布下使用跨网络评估来判断部署就绪度,而非仅依赖域内准确率。Deployment readiness should be assessed using cross-network evaluation under realistic class distributions, rather than within-domain accuracy alone, to suggest deployment readiness should be assessed using cross-network evaluation under realistic class distributions, rather than within-domain accuracy alone.

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
arXiv:2303.10130 评测基准 应用落地 OA · 绿色 被引 577 · S2

分析表明,借助 LLM,美国约 15% 的工作任务可在保持同等质量的前提下显著提速完成,意味着 LLM 驱动的软件将对底层模型经济影响的规模化产生实质性作用。The analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality, implying that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models.

Towards Expert-Level Medical Question Answering with Large Language Models
迈向基于大语言模型的专家级医学问答
arXiv:2305.09617 评测基准 应用落地 OA · 绿色 被引 808 · S2

结果表明,通过结合基础 LLM 改进(PaLM 2)、医学领域微调以及包括新颖集成精化方法在内的提示策略,医学问答正快速接近医生水平的表现。Results highlight rapid progress towards physician-level performance in medical question answering by leveraging a combination of base LLM improvements (PaLM 2), medical domain finetuning, and prompting strategies including a novel ensemble refinement approach.