Mastering LLM Evaluation: A Practical Guide for AI Engineers and Researchers (1) скачать в хорошем качестве

Mastering LLM Evaluation: A Practical Guide for AI Engineers and Researchers (1)

This technical deep dive explores rigorous methodologies for evaluating Large Language Models across multiple capability dimensions. From establishing evaluation objectives to implementing domain-specific assessments, we cover the complete evaluation taxonomy needed by AI practitioners. The presentation includes: The six critical evaluation objectives with current coverage analysis A four-dimensional evaluation taxonomy (knowledge, reasoning, task performance, alignment) Academic benchmark frameworks including MMLU and HELM with implementation details Domain-specific evaluation for mathematical reasoning using GSM8K and MATH benchmarks Code generation assessment with HumanEval and execution-based verification Advanced factuality assessment and hallucination detection methodologies Reasoning evaluation from Chain-of-Thought to Tree-of-Thought approaches Perfect for ML engineers, AI researchers, and technical teams implementing evaluation pipelines for foundation models in production environments. #LLMEvaluation #AIEngineering #TechnicalAI #ModelBenchmarking #MLOps #AIResearch #BenchmarkFrameworks #FactualityAssessment #ReasoningEvaluation #ChainOfThought