Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples

Sebastian Raschka's Blog offers insights, tutorials, and research updates on machine learning, deep learning, and artificial intelligence. Covering topics such as neural networks, data science, and Python programming, Sebastian Raschka's Blog provides resources for students, researchers, and practitioners in the field of AI. Developers can learn about  algorithms, research methodologies, and practical applications of machine learning through Raschka's blog posts and publications.

Sebastian Raschka

Explores four primary methods for evaluating large language models: multiple-choice benchmarks (like MMLU), verifier-based approaches for math and code, arena-style leaderboards using human preferences, and LLM-as-a-judge techniques. Provides from-scratch Python implementations for each method, demonstrating how to assess model performance using the Qwen3 model and tools like Ollama. Discusses the trade-offs of each approach—multiple-choice tests measure knowledge recall but lack real-world applicability, verifiers work well for deterministic domains, leaderboards capture user preferences but are resource-intensive, and LLM judges offer scalability but depend on rubric design. Emphasizes that effective evaluation combines multiple methods tailored to specific use cases.

Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)

Understanding the main evaluation methods for LLMs

Method 1: Evaluating answer-choice accuracy

Method 2: Using verifiers to check answers

Method 3: Judging responses with other LLMs