Eval Harness
An Eval Harness is a software framework or tool used to systematically evaluate the performance of machine learning models, particularly large language models (LLMs), by running them through standardized benchmarks and tests. It provides a consistent environment to measure metrics like accuracy, speed, and robustness across different models or configurations. These harnesses are essential for comparing models, tracking progress in AI research, and ensuring reproducibility in experiments.
Developers should use an Eval Harness when working on AI or machine learning projects that involve benchmarking models, such as in research, model development, or deployment scenarios. It is crucial for objectively assessing model capabilities, identifying strengths and weaknesses, and making informed decisions about model selection or improvements. Specific use cases include evaluating LLMs on tasks like text generation, question answering, or code completion, as well as compliance testing for safety and fairness.