Common Large Model Benchmarks
Benchmark Main Purpose MMLU (Massive Multitask Language Understanding) Evaluates the model’s multitask understanding ability across 57 academic domains HELM (Holistic Evaluation of Language Models) A comprehensive evaluation framework developed by Stanford, covering multiple tasks and fairness assessments BIG-Bench (BB) A large-scale benchmark with over 200 tasks developed by Google BBH (Big-Bench Hard) A harder subset of BIG-Bench, focusing on challenging tasks GSM8K (Grade School Math 8K) Evaluates the model’s ability to solve elementary-level math problems MATH A set of high school and university-level math problems to evaluate problem-solving abilities HumanEval A programming ability benchmark developed by OpenAI ARC (AI2 Reasoning Challenge) A reasoning challenge focused on scientific problem-solving abilities C-Eval A comprehensive evaluation benchmark designed for Chinese language models GLUE/SuperGLUE A general standard to assess natural language understanding TruthfulQA Tests the accuracy of model responses and reduces hallucinations FLORES A multilingual benchmark to evaluate machine translation capabilities AGIEval Tests high-difficulty standardized exam tasks that approach human cognitive abilities HELLASWAG Assesses the model’s ability to handle common-sense reasoning and logic Winogrande Tests the model’s ability to reason with common-sense knowledge MT-Bench Evaluates the model’s ability to engage in multi-turn conversations MLLM Benchmarks (LLaVA) A benchmark to assess large multimodal models’ image understanding capabilities