Granite Guardian
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
675903756f804e05e3cf41d6
Inkit Padhi
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2024-12-12T08:22:40.205Z
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Inkit
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Manish Nagireddy
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2024-12-11T13:39:39.816Z
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Manish Nagireddy
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Giandomenico Cornacchia
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2024-12-11T09:19:55.714Z
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Giandomenico Cornacchia
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Giandomenico
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Subhajit Chaudhury
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2024-12-11T13:45:46.974Z
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Subhajit Chaudhury
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Tejaswini Pedapati
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2024-12-11T13:45:41.001Z
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Tejaswini Pedapati
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Pierre Dognin
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2024-12-11T13:45:34.350Z
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Pierre Dognin
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pdognin
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Keerthiram Murugesan
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Erik Miehling
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2024-12-11T13:45:22.912Z
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Erik Miehling
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Martín Santillán Cooper
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2024-12-11T13:45:16.548Z
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Martín Santillán Cooper
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Kieran Fraser
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2024-12-11T13:45:08.457Z
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Kieran Fraser
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Giulio Zizzo
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2024-12-11T13:44:50.304Z
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Giulio Zizzo
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Muhammad Zaid Hameed
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2024-12-11T13:43:49.432Z
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Muhammad Zaid Hameed
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Mark Purcell
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2024-12-11T13:43:42.820Z
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Mark Purcell
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Allirog
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Michael Desmond
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2024-12-11T13:43:36.659Z
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Michael Desmond
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Qian Pan
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2024-12-11T13:43:24.581Z
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Qian Pan
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Inge Vejsbjerg
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Elizabeth M. Daly
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2024-12-11T13:43:02.426Z
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Elizabeth Daly
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Michael Hind
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2024-12-11T13:40:49.497Z
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Michael Hind
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Werner Geyer
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Ambrish Rawat
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2024-12-11T13:40:24.628Z
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Ambrish Rawat
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Kush R. Varshney
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2024-12-11T13:40:17.867Z
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Kush Varshney
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Prasanna Sattigeri
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2024-12-11T13:39:33.098Z
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Prasanna Sattigeri
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Granite Guardian
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
Granite Guardian models provide comprehensive risk detection for prompts and responses in large language models (LLM), addressing social bias, profanity, violence, sexual content, unethical behavior, jailbreaking, and retrieval-augmented generation (RAG) hallucination risks.
Granite Guardian models large language model (LLM) risk detection social bias profanity violence sexual content unethical behavior jailbreaking retrieval-augmented generation (RAG) hallucination risks context relevance groundedness answer relevance AUC scores