# LLM Evaluation Dataset Cards and Metadata
Status: public
Confidence: medium (0.865) (verified)
Last verified: 2026-06-02
Generation: ai_structured


## TL;DR

Dataset cards and machine-readable metadata make LLM evaluations auditable by recording what was tested, how examples were selected, and how scores should be interpreted.

## Core Explanation

LLM evaluation data is not just a JSONL file of prompts. A useful eval corpus needs provenance, license, intended use, exclusion rules, label definitions, annotator guidance, splits, version IDs, and known limitations.

For agents, this metadata matters because evaluation failures often come from ambiguous examples or stale labels. Dataset cards and structured metadata let teams trace a score back to the source and decide whether a regression is model behavior, data drift, or benchmark maintenance.

## Source-Mapped Facts

- Hugging Face documentation describes the README.md file in a dataset repository as the dataset card. ([source](https://huggingface.co/docs/hub/datasets-cards))
- MLCommons documentation describes Croissant as a metadata format for machine learning datasets. ([source](https://docs.mlcommons.org/croissant/docs/croissant-spec.html))
- OpenAI Evals documentation describes evals as using data sources and graders to measure model behavior. ([source](https://developers.openai.com/api/docs/guides/evals))

## Further Reading

- [Hugging Face Dataset Cards](https://huggingface.co/docs/hub/datasets-cards)
- [MLCommons Croissant Specification](https://docs.mlcommons.org/croissant/docs/croissant-spec.html)
- [OpenAI Evals Guide](https://developers.openai.com/api/docs/guides/evals)