Granite Guardian
Authors: name: Inkit Padhi, name: Manish Nagireddy, name: Giandomenico Cornacchia, name: Subhajit Chaudhury, name: Tejaswini Pedapati, name: Pierre Dognin, name: Keerthiram Murugesan, name: Erik Miehling, name: Martín Santillán Cooper, name: Kieran Fraser, name: Giulio Zizzo, name: Muhammad Zaid Hameed, name: Mark Purcell, name: Michael Desmond, name: Qian Pan, name: Inge Vejsbjerg, name: Elizabeth M. Daly, name: Michael Hind, name: Werner Geyer, name: Ambrish Rawat, name: Kush R. Varshney, name: Prasanna Sattigeri

We introduce the Granite Guardian models, a suite of safeguards designed to
provide risk detection for prompts and responses, enabling safe and responsible
use in combination with any large language model (LLM). These models offer
comprehensive coverage across multiple risk dimensions, including social bias,
profanity, violence, sexual content, unethical behavior, jailbreaking, and
hallucination-related risks such as context relevance, groundedness, and answer
relevance for retrieval-augmented generation (RAG). Trained on a unique dataset
combining human annotations from diverse sources and synthetic data, Granite
Guardian models address risks typically overlooked by traditional risk
detection models, such as jailbreaks and RAG-specific issues. With AUC scores
of 0.871 and 0.854 on harmful content and RAG-hallucination-related benchmarks
respectively, Granite Guardian is the most generalizable and competitive model
available in the space. Released as open-source, Granite Guardian aims to
promote responsible AI development across the community.
  https://github.com/ibm-granite/granite-guardian