Mastering LLM Evaluation: A Practical Guide for AI Engineers and Researchers (2)
Автор: Deep Wing
Загружено: 2025-04-27
Просмотров: 87
Discover cutting-edge methodologies for comprehensive LLM evaluation in this technical deep dive. This session explores task-specific performance metrics, safety boundary assessment, robustness testing, human evaluation protocols, computational efficiency analysis, and systematic evaluation frameworks—essential knowledge for AI engineers implementing production-grade evaluation pipelines.
Learn how to implement format compliance assessment, refusal consistency testing, input perturbation analysis, pairwise comparison frameworks, throughput measurement protocols, and more. We cover best practices including triangulation methodology, statistical rigor, reproducibility infrastructure, and version control, plus emerging research in judge model optimization and multi-agent assessment.
Perfect for ML engineers, AI researchers, and technical teams building reliable evaluation systems for large language models in production environments.
#LLMEvaluation #AIEngineering #ModelBenchmarking #SafetyAlignment #RobustnessEvaluation #ComputationalEfficiency #HumanEvaluation #MLOps #TechnicalAI #AIResearch
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