AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
The results show that high self-consistency does not necessarily indicate high agreement with human judgments when using local LLMs as automatic judges, and highlight the need to evaluate both consistency and human alignment when using local LLMs as automatic judges.
Overall, the findings suggest that LLMs can approximate human coding in this case-specific setting, particularly at the coding level, and may serve as a scalable support tool for inductive qualitative analysis.
A suite of automated tools for automated batch processing that provide decision rationales and evidence enhances transparency and allows for human verification of AI decisions and provides a suite of automated tools for key SR tasks.
Although LLM-based benchmark ratings can approximate expert judgments and reduce the need for labor-intensive human triple coding, limitations remain regarding cost, genre- and task specificity, and sensitivity to text presentation and student grade level—factors that constrain immediate classroom use, particularly for...
This survey provides a comprehensive overview of recent advances in LLM-based evaluation, covering techniques, applications, and challenges across domains, with future directions emphasizing standardized protocols, uncertainty estimation, and human–AI collaboration.
Overall, the results show that prompt configuration and model choice jointly influence phishing detection performance, false-alarm rates, output reliability, and local processing efficiency.
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