LLM model testing benchmarks and metrics

https://wiki-stock.win/index.php/LLM_model_testing_benchmarks_and_metrics

LLM model testing benchmarks and metrics evaluate language model performance across tasks like reasoning, translation, and coding. Widely used benchmarks include MMLU, HellaSwag, and HumanEval, assessing knowledge, common sense, and code generation. Metrics such as accuracy, F1 score, perplexity, and BLEU score quantify model quality, though they may not fully capture real-world reliability. Developers and researchers rely on these tools to compare models and guide improvements.