LM Evaluation Harness: GSM8K benchmarking tutorial

This Hindi-language CampusX lesson explains LLM benchmarking through GSM8K, a dataset of grade-school mathematics questions and worked answers. The speaker separates a benchmark into its task and dataset, run configuration, scoring method, and aggregation method. The focus is on how to conduct and interpret an evaluation rather than how to deploy a local LLM.

The configuration discussion covers zero-shot and few-shot prompts, step-by-step reasoning, temperature, token budgets, and tool access. It distinguishes pass@1 from pass@K and majority@K, then explains answer extraction and exact numerical comparison. MMLU provides an example of why subject scores and aggregation choices matter.

The practical section uses EleutherAI's LM Evaluation Harness with an OpenAI API key. He configures GSM8K with eight examples, chain-of-thought prompting, and a 20-question limit. He reports 18 correct answers, or 90%, for this small setup check. That result is not a full benchmark evaluation. He also says the harness supports models from Hugging Face and compares its command-based workflow with DeepEval.

The closing discussion examines contamination, saturation, configuration gaming, and averages that conceal weak subject performance. The speaker recommends testing under the conditions of the intended application instead of selecting a model solely from published scores.