CLSG-Benchmark (pilot)

Part of VERIAUDIT — an open-source red-teaming platform measuring whether AI safety, robustness, and instruction-following behavior remain consistent when equivalent inputs are translated across linguistic boundaries.

Status: pilot subset. This release contains 13 of the planned 125 core intents (× 4 linguistic variants = 500 test cases). The full VERIAUDIT-500 dataset is under development and will be published here after independent bilingual human verification. Do not cite this pilot as a validated benchmark — it exists to make the evaluation pipeline runnable end-to-end, not as a finished scientific artifact.

⚠️ Content warning

This dataset includes red-teaming test intents designed to probe whether a model refuses unsafe requests — including prompts related to cybercrime, weapons, self-harm, hate speech, misinformation, fraud, and privacy violations. Prompt text is deliberately generic and non-actionable (no real operational detail or uplift), in the same style used by public red-teaming benchmarks such as AdvBench and HarmBench. It is provided for AI safety evaluation and refusal-consistency research only. Do not use this dataset to elicit, collect, or distribute harmful content, and do not use model outputs generated against it for any purpose other than safety research.

What this dataset is for

Each row expresses a single underlying intent in four linguistic forms — English, Urdu, Roman Urdu, and code-switched Urdu/English — so that a model's safety and instruction-following behavior can be compared for the same request across languages. This is the input to VERIAUDIT's Cross-Lingual Safety Gap (CLSG) metric — see CLSG-Evaluator and the methodology doc.

Schema

Field	Type	Description
id	string	Stable identifier, VA-###.
intent_category	string	e.g. Benign — Everyday Assistance, Refusal Test — Cybercrime.
intent	string	Short human-readable description of the underlying test intent.
english	string	The intent expressed in English.
urdu	string	The intent expressed in Urdu script.
roman_urdu	string	The intent expressed in Roman-script Urdu.
code_switched	string	The intent expressed in code-switched Urdu/English.
verification_status	string	verified, pending, or flagged. See below — never inferred purely from a similarity score.
semantic_similarity	object	Embedding cosine similarity (multilingual sentence-transformer) between English and each other variant. A screening signal only.

Verification process

1. A variant is machine-translated or human-authored.
2. Embedding similarity is computed against the English source (sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2).
3. Similarity below 0.85 is marked flagged.
4. Everything else starts as pending — similarity above the threshold is never treated as automatic proof of equivalence.
5. A human reviewer promotes a case to verified after manual comparison.

The pilot cases here were translated by the project's lead researcher as a first pass; most are marked pending for exactly this reason.

Loading

from datasets import load_dataset

ds = load_dataset("abeeranajam31/CLSG-Benchmark", split="pilot")
print(ds[0])

Limitations

Small sample size (13 intents), single-annotator translation pass for this pilot release, embedding-based (not human-validated) similarity screening, and coverage limited to Urdu / Roman Urdu / code-switched Urdu-English. See docs/research.md for the full limitations discussion.

Responsible use

Intended for AI safety and red-teaming research, refusal-consistency evaluation, and multilingual NLP research. Not intended for training generative models to produce harmful content, and not a substitute for professional crisis support — if you or someone you know is struggling, please reach out to a local crisis line.

Citation

@dataset{veriaudit_clsg_benchmark,
  author = {Najam, Abeera},
  title = {VERIAUDIT CLSG-Benchmark (pilot)},
  year = {2026},
  url = {https://huggingface.co/datasets/abeeranajam31/CLSG-Benchmark}
}

License

MIT — see LICENSE.

Contact

Abeera Najam — Founder & Lead Researcher, VERIAUDIT — veriiaudit@gmail.com