README.md

metadata

license: other
task_categories:
  - text-classification
  - text-retrieval
language:
  - en
pretty_name: S2ORC Safety
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: main/*.parquet

S2ORC Safety

This dataset is a filtered and enriched subset of an S2ORC computer science paper corpus, focused on AI safety and adjacent safety-relevant research.

It contains 16,806 papers selected through:

1. local embedding generation
2. clustering
3. GPT-5.4 mini cluster-level screening
4. GPT-5.4 mini paper-level labeling
5. a rescue relabel pass on suspicious exclusions
6. structured metadata extraction over the accepted paper set
7. filtering out 304 rows that were missing both parsed_title and abstract

Main Files

• main/*.parquet

  • sharded full enriched source rows
  • extracted metadata
  • normalized GitHub repo links
  • Hugging Face code mirror links
  • normalized model / dataset / metric / scalar fields

• metadata/*.parquet

  • sharded metadata extraction only

• paper_metadata_summary_normalized.json

  • corpus-level summary statistics over the normalized metadata fields

• code_links/*.parquet

  • sharded paper-to-code join table with normalized GitHub URLs and HF mirror paths

Contents

The main parquet includes:

• original enriched paper fields from the source corpus

  • title, abstract, full text, sections, references, authors, venue metadata, URLs
  • extracted source-side fields like summary, methods, results, models, datasets, metrics, limitations, training_details

• metadata extraction fields

  • reproducibility:
    • repro_steps_json
    • setup_requirements_json
    • training_or_eval_recipe_json
    • artifact_availability_json
    • code_urls_json
    • dataset_urls_json
    • model_urls_json
  • safety taxonomy:
    • safety_area_json
    • attack_or_defense_json
    • threat_model_json
    • target_system_json
    • harm_type_json
  • experimental details:
    • target_models_json
    • datasets_benchmarks_json
    • baselines_compared_json
    • evaluation_metrics_json
    • main_results_json
    • claimed_contributions_json
  • practicality:
    • compute_requirements_json
    • runtime_cost
    • human_eval_required
    • closed_model_dependency
    • deployment_readiness
    • replication_difficulty
    • extraction_confidence

• normalized fields

  • setup_requirements_norm_json
  • target_models_norm_json
  • datasets_benchmarks_norm_json
  • baselines_compared_norm_json
  • evaluation_metrics_norm_json
  • runtime_cost_norm
  • human_eval_required_norm
  • closed_model_dependency_norm
  • deployment_readiness_norm
  • replication_difficulty_norm

• code link fields

  • github_repo_urls_json
  • hf_code_paths_json
  • hf_code_web_urls_json
  • github_repo_count
  • hf_code_repo_count

Missing Values

• missing list-like fields are stored as empty JSON arrays
• missing scalar categorical fields are stored as "None specified"

Notes

• This is a broad-tent AI safety dataset rather than a narrow alignment-only dataset.
• The labeling and extraction steps were LLM-assisted and should be treated as high-utility annotations, not ground truth.
• Process-only columns used to build the release were removed from the published parquet.
• The companion code mirror is published separately as AlgorithmicResearchGroup/s2orc-safety-code.
• Normalization is conservative. It collapses obvious duplicates like CIFAR10 / CIFAR-10, ResNet50 / ResNet-50, and accuracy / Accuracy, but does not try to solve full ontology matching.