LLM Evaluation Techniques in Practice

This beginner-level path introduces practical methods for benchmarking large language models on multiple-choice question answering, summarization, and other text generation tasks. You will compare model outputs using fuzzy matching, ROUGE, and embedding-based semantic similarity. You will also examine internal scoring signals such as token log probabilities and perplexity to assess output likelihood and fluency. Guided experiments explore token efficiency, temperature sensitivity, output consistency, prompting strategies, and hallucination detection. This path is designed for learners who want a hands-on foundation in evaluating and comparing language models beyond basic accuracy scores.