{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/granite-guardian","title":"Granite Guardian","arxiv_id":"2412.07724","date":"2024-12-10","proceeding":null,"authors":["Inkit Padhi","Manish Nagireddy","Giandomenico Cornacchia","Subhajit Chaudhury","Tejaswini Pedapati","Pierre Dognin","Keerthiram Murugesan","Erik Miehling","Martín Santillán Cooper","Kieran Fraser","Giulio Zizzo","Muhammad Zaid Hameed","Mark Purcell","Michael Desmond","Qian Pan","Zahra Ashktorab","Inge Vejsbjerg","Elizabeth M. Daly","Michael Hind","Werner Geyer","Ambrish Rawat","Kush R. Varshney","Prasanna Sattigeri"],"abstract":"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. 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