LLM Evaluation
LLM evaluation is a systematic methodology for assessing the performance, capabilities, and limitations of large language models (LLMs) across various tasks and benchmarks. It involves designing and applying metrics, datasets, and testing frameworks to measure aspects like accuracy, coherence, bias, safety, and efficiency in model outputs. This process is crucial for comparing different models, guiding development improvements, and ensuring reliable deployment in real-world applications.
Developers should learn LLM evaluation when building, fine-tuning, or deploying LLMs to ensure models meet quality standards and avoid harmful outputs in production systems. It is essential for tasks like benchmarking against state-of-the-art models, validating fine-tuned models for specific domains (e.g., healthcare or finance), and complying with ethical AI guidelines by detecting biases or safety issues. This skill is particularly valuable in research, AI product development, and regulatory compliance roles.