EdyVision/pii-skills-ablation-results

PII Skills Ablation — Scored Results

This repository contains model predictions and evaluation scores for the ablation study described in:

"When Parametric Knowledge Wins: A Controlled Ablation of Agent Skills and Tool Use for PII Detection in Small Language Models"

Results are produced by running four open-weight instruction-tuned models (Gemma 2 9B, Llama 3.1 8B, Mistral 7B, Qwen 2.5 7B) under four conditions (zero-shot, +Docs, +Tool, +Skills) on the benchmark in EdyVision/pii-skills-ablation, with scoring against PII-Codex-aligned ground truth.

Dataset Summary

Overview

This dataset holds model predictions and per-sample evaluation scores (e.g. F1, precision, recall) for the ablation study. One row corresponds to one (sample, model, condition) run. Schema is fixed across splits for consistent loading.

Key Characteristics

• One row per (sample, model, condition)
• Predictions stored as JSON lists of spans; scores as JSON strings
• Splits by run type and prompt version (e.g. pilot_v1, main_v1)

Dataset Structure

Column	Type	Description
sample_id	string	Identifier for the benchmark sample (matches benchmark id).
source	string	Source dataset: ai4privacy, nvidia_nemotron_pii, or gretel_pii_masking.
model	string	Model identifier (e.g. gemma2_9b, llama3_8b, mistral_7b, qwen2_7b).
condition	string	Ablation condition: zero_shot, with_docs, with_tool, or with_skills.
prompt_version	string	Prompt version used (e.g. v1).
run_type	string	Run type: pilot or main.
predictions	string	JSON list of model-predicted spans (see span structure below).
scores	string	JSON string of evaluation metrics (e.g. F1, precision, recall) for that sample.
error	string	Error message if the run failed; empty otherwise.
tool_executed	bool	Whether a tool was invoked (for +Tool / +Skills).
skill_viewed	bool	Whether the Skill document was used (for +Skills).
elapsed_seconds	float	Wall-clock time for the run.
conversation_turns	int	Number of turns (for multi-turn conditions).

Scores are computed against the benchmark ground truth after full label alignment to PII-Codex types (see paper and code repo).

Prediction span structure

The predictions column is a JSON-encoded list of spans. Each span matches the structure used in the benchmark and source data:

{
  "start": 12,
  "end": 24,
  "label": "EMAIL_ADDRESS"
}

start and end are character offsets into the sample text; label is a PII-Codex type name. Parse the column with json.loads() before use.

Intended Use

• Primary: Reproducibility and extended analysis for the ablation study (aggregate statistics, plots, and tables in the paper are produced from these results).

• Secondary: Re-analysis, meta-analysis, or comparison with other PII detection systems on the same benchmark. Not intended as training data.

• Out-of-Scope Uses: Not intended as training data. Use only with the same benchmark and alignment for comparable numbers.

Citations

If you use these results, please cite the paper and, where applicable, the results dataset:

@article{rosado2026context,
  title   = {When Parametric Knowledge Wins: A Controlled Ablation of Agent Skills and Tool Use for PII Detection in Small Language Models},
  author  = {Rosado, Eidan J.},
  year    = {2026},
  note    = {Scored results: \url{https://huggingface.co/datasets/EdyVision/pii-skills-ablation-results}}
}

Related citations

If you also use the benchmark or run experiments on it, please cite the benchmark and its source datasets as described in the benchmark datacard.

Ethical Considerations

• Results contain model outputs on PII-related text. Use for research and reproducibility only; handle any residual sensitivity in downstream use responsibly.

Limitations

• Results depend on the exact benchmark version, prompt version, and PII-Codex alignment used in the study; different versions may yield different numbers.
• Scores are stored as JSON strings for schema consistency across splits; parsing is required for numerical analysis.
• Pilot and main splits reflect different sample sizes (e.g. n=200 vs n=2,000); use the split that matches the analysis you are reproducing.

License

See the dataset repository and the linked paper/code repository for license terms. These results are provided for research and reproducibility.

Related Resources

• Benchmark and config: EdyVision/pii-skills-ablation
• Code and notebooks: EdyVision/pii-skills-ablation-study