llm-evaluation

Evaluate LLM performance by calculating automated metrics like BLEU, ROUGE, and BERTScore on your test cases. This framework automates these quantitative measurements to provide immediate numerical analysis of text generation quality.

The LLM-as-Judge pattern leverages powerful LLMs to objectively evaluate model responses. This approach scales up quality measurement and assessment without requiring extensive manual human evaluation for every test case.

Set up LLM benchmarking by statistically comparing model variants to detect performance degradation. This framework provides A/B testing and regression capabilities to ensure new chatbot deployments do not regress in quality.

Yes, this evaluation framework supports text generation, classification, and RAG evaluation. It provides detailed metric implementations tailored for these specific use cases to measure accuracy and response effectiveness.

Yes, you can use BERTScore and ROUGE together for text generation evaluation. The framework integrates both metrics, allowing you to combine semantic similarity scores with n-gram overlap analysis for comprehensive testing.

Use human evaluation over automated LLM metrics when you need nuanced quality assessment that quantitative scores cannot capture. This framework facilitates incorporating human judgment alongside automated metrics for robust evaluation.