How to evaluate LLM apps
The goal of evaluating LLMs is to understand their strengths and weaknesses, so as to enhance accuracy and efficiency while reducing errors, thereby maximizing their usefulness in solving real-world problems. This evaluation process typically occurs offline during the development phase. Offline evaluations provide initial insights into model performance under controlled test conditions and include aspects such as hyperparameter tuning and benchmarking against peer models or established standards. They offer a necessary first step toward refining a model before deployment.
While human assessments are sometimes seen as the gold standard, they are hard to scale and require careful design to avoid bias from subjective preferences or authoritative tones. There are many standardized benchmarks such as MBPP to test basic programming skills, while GSM8K is utilized for multi-step mathematical reasoning. API-Bank evaluates models’ aptitudes for making decisions...
