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upvoted a paper about 15 hours ago
MM-LLMs: Recent Advances in MultiModal Large Language Models reacted to pbhappliedsystems's post with 👀 1 day ago
🚀 **New flagship dataset — and an argument about what a dataset card should be.**
Most synthetic datasets on the Hub ship row counts, a license, and little else — pipeline opaque, rejection criteria unstated, compliance unaudited. We published the opposite.
**SynthEval Cloud — Regulated-Domain Synthetic Instruction Dataset**
👉 https://huggingface.co/datasets/pbhappliedsystems/syntheval-cloud-regulated-instruct-1k
**1,116** quality-gated instruction records across **7 regulated domains** (medical, legal, GDPR, privacy, education, e-commerce, transport). Every record cleared a documented cascade, not a vibe check:
- 🧪 **Dual-signal hallucination gate** — rejects only when embedding cosine *and* keyword-overlap both fail; a low score alone never rejects.
- 🔒 **Layered PII masking + independent leak audit** — a separate over-reporting scanner found **0.0% residual leak** across all 1,116 records.
- 📊 **Whole-corpus evaluation, not a sample** — MATTR **0.769**, mean cosine **0.73**, **0%** near-duplicates, **96.9%** yield.
- 🧾 **The 36 rejections ship too**, each tagged with its failing gate. Removal at the gate is the product; we show our work.
Every number on the card is a field in the `evaluation_report.json` shipped beside the data — full methodology + provenance (Mistral-Nemo AWQ W4A16 · vLLM 0.8.5.post1 · Modal A10G).
One release from **SynthEval**: Studio (local GPU) + Cloud (Modal+vLLM), proving quality parity across substrates.
📄 Whitepaper: https://pbhappliedsystems.com/SynthEval_Studio_and_Cloud_Quality-Gated_Synthetic_Data_Generation.pdf
🔎 Overview: https://pbhappliedsystems.com/synthetic-data.html
**CC BY 4.0** — commercial use welcome, just credit it. Need defensible synthetic data at scale? Let's talk.
— Patrick Hill, PBH Applied Systems updated a Space 1 day ago
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