Granite Guardian

The Granite Guardian models are a collection of models designed to detect risks in prompts and responses. Trained on instruction fine-tuned Granite languages models, these models can help with risk detection along many key dimensions catalogued in the IBM AI Risk Atlas. These models are trained on unique data comprising human annotations from socioeconomically diverse people and synthetic data informed by internal red-teaming. They outperform similar models on standard benchmarks.

Quick Links

• Granite Guardian Collection:
  • 🤗 Granite-Guardian-3.0-8B
  • 🤗 Granite-Guardian-3.0-2B
  • 🤗 Granite-Guardian-HAP-125M
  • 🤗 Granite-Guardian-HAP-38M
• Granite Guardian Recipes:
  • 📕 Quick Start Guide (walkthrough video ▶️)
  • 📕 Detailed Guide
  • 📕 Usage Governance Workflow
  • 📕 Hate, Abuse, and Profanity (HAP) Detection
• Demos:
  • ▶️ Feature Demo
• Additional Resources:
  • 🤗 Datasets - SocialStigmaQA, SocialStigmaQA-JA, AttaQ, ProvoQ, WikiContradict
• Website: Granite Guardian Docs
• License: Apache 2.0

Usage

Intended use

Granite Guardian is useful for risk detection use-cases which are applicable across a wide-range of enterprise applications -

• Detecting harm-related risks within prompt text or model response (as guardrails). These present two fundamentally different use cases as the former assesses user supplied text while the latter evaluates model generated text.
• RAG (retrieval-augmented generation) use-case where the guardian model assesses three key issues: context relevance (whether the retrieved context is relevant to the query), groundedness (whether the response is accurate and faithful to the provided context), and answer relevance (whether the response directly addresses the user's query).

Scope of Use

• Granite Guardian models must only be used strictly for the prescribed scoring mode, which generates yes/no outputs based on the specified template. Any deviation from this intended use may lead to unexpected, potentially unsafe, or harmful outputs. The model may also be prone to such behaviour via adversarial attacks.
• The model is targeted for risk definitions of general harm, social bias, profanity, violence, sexual content, unethical behavior, jailbreaking, or groundedness/relevance for retrieval-augmented generation. It is also applicable for use with custom risk definitions, but these require testing.
• The model is only trained and tested on English data.
• Given their parameter size, the main Granite Guardian models are intended for use cases that require moderate cost, latency, and throughput such as model risk assessment, model observability and monitoring, and spot-checking inputs and outputs. Smaller models, like the Granite-Guardian-HAP-38M for recognizing hate, abuse and profanity can be used for guardrailing with stricter cost, latency, or throughput requirements.

Evaluations

Harm Benchmarks

Granite Guardian 3.0 models outperforms competitive models across the standard benchmarks of Aeigis AI Content Safety Dataset, ToxicChat, HarmBench, SimpleSafetyTests, BeaverTails, OpenAI Moderation data, SafeRLHF and xstest-response. With the risk definition set to jailbreak, both models achieve a recall of 1.0 for the jailbreak prompts within ToxicChat dataset. Following plot compares F1 scores for different models across the benchmark datasets.

RAG Hallucination Benchmarks

For risks in RAG use cases, the Granite-Guardian-3.0-8B model outperforms open-source models on TRUE benchmarks and is only second to the non-commercial models.

Training Data

Granite Guardian 3.0 models are trained on a combination of human annotated and synthetic data. Samples from hh-rlhf dataset were used to obtain responses from Granite and Mixtral models. These prompt-response pairs were annotated for different risk dimensions by a group of people at DataForce. DataForce prioritizes the well-being of its data contributors by ensuring they are paid fairly and receive livable wages for all projects. Additional synthetic data was used to supplement the training set to improve performance for hallucination and jailbreak related risks.

Annotator Demographics

Year of Birth	Age	Gender	Education Level	Ethnicity	Region
Prefer not to say	Prefer not to say	Male	Bachelor	African American	Florida
1989	35	Male	Bachelor	White	Nevada
Prefer not to say	Prefer not to say	Female	Associate's Degree in Medical Assistant	African American	Pennsylvania
1992	32	Male	Bachelor	African American	Florida
1978	46	Male	Bachelor	White	Colorado
1999	25	Male	High School Diploma	Latin American or Hispanic	Florida
Prefer not to say	Prefer not to say	Male	Bachelor	White	Texas
1988	36	Female	Bachelor	White	Florida
1985	39	Female	Bachelor	Native American	Colorado / Utah
Prefer not to say	Prefer not to say	Female	Bachelor	White	Arkansas
Prefer not to say	Prefer not to say	Female	Master of Science	White	Texas
2000	24	Female	Bachelor of Business Entrepreneurship	White	Florida
1987	37	Male	Associate of Arts and Sciences - AAS	White	Florida
1995	29	Female	Master of Epidemiology	African American	Louisiana
1993	31	Female	Master of Public Health	Latin American or Hispanic	Texas
1969	55	Female	Bachelor	Latin American or Hispanic	Florida
1993	31	Female	Bachelor of Business Administration	White	Florida
1985	39	Female	Master of Music	White	California