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name: rouge_l_evaluation description: Compute deterministic ROUGE-L style generation metrics for Natural Instructions recovery outputs and ablations.

Rouge L Evaluation

Use this skill when reconstructing the BART0/Natural Instructions cross-task generalization workflow without relying on the original paper repository. Do not use it for unrelated instruction-following papers unless the task uses a Natural-Instructions-style schema, task-level held-out evaluation, and generation metrics.

Inputs

• Paper-derived module document and module_plan.json.
• Small task records or instruction items supplied by the current recovery attempt.
• Optional output paths for deterministic JSON artifacts.

Outputs

• Deterministic JSON or Python return values that can be consumed by downstream recovery scripts.
• Validation evidence from the included tests.

Workflow

1. Read the current attempt inputs and keep source use inside the allowed recovery boundary.
2. Apply the module contract exactly as described by the paper-derived module document.
3. Write machine-readable artifacts when a CLI output path is provided.
4. Cross-check downstream consumers with tests before accepting recovery evidence.

Validation

Run python <distiller>/module-to-skill/scripts/validate_skill_tree.py <skill_dir> --run-tests from any working directory. The included tests are standard-library compatible and do not require the original source repository.

Limitations

This skill preserves a reusable mechanism from the paper but does not by itself reproduce full BART-base training on all 61 Natural Instructions tasks. Full recovery requires a resolved dataset and model runtime; reduced recovery must remain explicitly marked as proxy evidence.