evaluation-metrics

LLM evaluation applies standardized metrics like exact-match and token-overlap to structured datasets, enabling reproducible assessments across task scenarios such as summarization, translation, and question answering. This Skill automates metric computation on your predictions.

Reproducible benchmarks require structured evaluation datasets with examples and metadata, standardized metric implementations, and experiment tracking for A/B testing. This Skill provides data models and metrics tools to automate these assessments consistently.

Yes. This Skill includes experiment tracking for A/B testing and model comparisons, applying metrics like exact-match and token-overlap across your evaluation dataset to produce aggregated results for side-by-side assessment.

Exact-match metrics measure whether predictions perfectly match reference outputs, while token-overlap metrics assess partial similarity by counting shared tokens. Both are computed on the same evaluation dataset to provide complementary views of LLM performance.

Yes. This Skill uses structured data models (EvalDataset, EvalExample) that define examples with metadata. Datasets are saved as JSON files and must conform to these models to enable reproducible metric computation and experiment tracking.

This evaluation framework applies across summarization, translation, question answering, and other NLP benchmarks. Standardized metrics and dataset structures support consistent assessment across multiple task scenarios and model variants.