LLM Benchmarks: Everything on MMLU, HellaSwag, BBH, and Beyond

LLM benchmarks provide a structured framework for evaluating Large Language Models (LLMs) across various tasks, enabling comparisons of model performance and identification of gaps in knowledge. These standardized tests assess LLMs on skills such as reasoning, comprehension, coding, conversation, translation, math, logic, and standard educational assessments like SAT or ACT. Different benchmarks focus on specific domains, including common-sense reasoning (HellaSwag), language understanding (MMLU), and conversation (Chatbot Arena). However, existing benchmarks often lack domain relevance and specificity, leading to limitations in their effectiveness. To overcome these challenges, synthetic data generation emerges as a valuable solution for creating adaptable, domain-specific benchmarks that stay relevant over time. LLM benchmarking provides a standardized framework for evaluating model performance, aligning with objectives, embracing task diversity, and staying domain-relevant. By leveraging DeepEval, users can easily access and use various benchmarks, including MMLU, HellaSwag, and BIG-Bench Hard, to evaluate their custom LLMs and gain valuable insights into their strengths and areas for enhancement.