AI Model Evaluation: Benchmarking Chinese Models with Eval
How to scientifically evaluate and compare output quality across different models, establishing benchmarks and scoring systems.
Why Evaluation Is Needed
Model vendor benchmarks may not match your actual use case. You need to evaluate model quality on your real tasks to make accurate selection decisions.
Evaluation Metrics
Code generation: syntax correctness, logic correctness, readability; Q&A: relevance, accuracy, completeness; Translation: accuracy, BLEU score, human evaluation; Dialogue: naturalness, task completion rate.
Automated Evaluation Frameworks
Use LLM-as-Judge (evaluating weaker model outputs with a stronger model) or human evaluation. For high-value outputs, combine human and automated evaluation.
Evaluation Datasets
Build your own business evaluation dataset (e.g., 100 typical tasks), run each model through it, and compare results. Update evaluation sets regularly to reflect real user scenarios.