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データ・研究 · SKILL.md

gaia-evaluation

gaia-evaluation

Guides OWL's GAIA benchmark data preparation, level and split selection, result persistence, final-answer extraction, and answer scoring.

Independent GitHub snapshot · Automated classification · Original documentation governs

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GitHub

name: gaia-evaluation description: "Guides OWL's GAIA benchmark data preparation, level and split selection, result persistence, final-answer extraction, and answer scoring." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

OWL GAIA Evaluation

Use this route for GAIABenchmark, GAIA validation/test runs, task-level selection, attached files, result JSON, answer normalization, or accuracy summaries. A meaningful benchmark run requires GAIA data, model-provider credentials, network access, and potentially long execution; package import alone is not benchmark reproduction.

Before running

  • Prepare a private data directory and result path. The loader expects 2023/validation and 2023/test directories containing metadata.jsonl or metadata.parquet plus referenced task files.
  • Read evaluation-workflow.md for the exact constructor and run arguments, and answer-normalization.md for focused scoring fixtures. The accepted split values are valid and test; train is intentionally rejected because GAIA has no training set in this implementation.
  • Configure the model backends through workforce-workflows, and route attached document/image/table behavior to document-processing.
  • Start with a small subset or explicit idx; use save_result=True only with a result file you can safely resume and inspect.

Evaluation contract

GAIABenchmark.run prepares each task, creates OwlGAIARolePlaying, runs the society loop, extracts final_answer tags, scores against the ground truth, and returns a summary containing total, correct, results, and accuracy. The scorer has separate numeric, comma/semicolon-list, and normalized-string paths. Preserve the requested answer format exactly; do not add prose, units, or markdown when the task expects a scalar or list.

Missing attachments are skipped with a zero-result record by the source implementation. Treat that as a data-preparation signal and fix the data path; do not interpret it as evidence that a model answered correctly. Read troubleshooting.md for download, resume, normalization, and cost failures. The reported OWL/GAIA scores in project materials are historical claims, not a result verified by this skill run.

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