The rapid advancements in large language models (LLMs) have introduced significant opportunities for various industries. However, their deployment in real-world scenarios also presents challenges, such as generating harmful content, hallucinations, and potential ethical misuse. LLMs can produce socially biased, violent, or profane outputs, and adversarial actors often exploit vulnerabilities through jailbreaks to bypass safety measures. Another critical issue lies in retrieval-augmented generation (RAG) systems, where LLMs integrate external data but may provide contextually irrelevant or factually incorrect responses. Addressing these challenges requires robust safeguards to ensure responsible and safe AI usage.
To address these risks, IBM has introduced Granite Guardian, an open-source suite of safeguards for risk detection in LLMs. This suite is designed to detect and mitigate multiple risk dimensions. The Granite Guardian suite identifies harmful prompts and responses, covering a broad spectrum of risks, including social bias, profanity, violence, unethical behavior, sexual content, and hallucination-related issues specific to RAG systems. Released as part of IBM’s open-source initiative, Granite Guardian aims to promote transparency, collaboration, and responsible AI development. With comprehensive risk taxonomy and training datasets enriched by human annotations and synthetic adversarial samples, this suite provides a versatile approach to risk detection and mitigation.
Technical Details
Granite Guardian’s models, based on IBM’s Granite 3.0 framework, are available in two variants: a lightweight 2-billion parameter model and a more comprehensive 8-billion parameter version. These models integrate diverse data sources, including human-annotated datasets and adversarially generated synthetic samples, to enhance their generalizability across diverse risks. The system effectively addresses jailbreak detection, often overlooked by traditional safety frameworks, using synthetic data designed to mimic sophisticated adversarial attacks. Additionally, the models incorporate capabilities to address RAG-specific risks such as context relevance, groundedness, and answer relevance, ensuring that generated outputs align with user intents and factual accuracy.
A notable feature of Granite Guardian is its adaptability. The models can be integrated into existing AI workflows as real-time guardrails or evaluators. Their high-performance metrics, including AUC scores of 0.871 and 0.854 for harmful content and RAG-hallucination benchmarks, respectively, demonstrate their applicability across diverse scenarios. Furthermore, the open-source nature of Granite Guardian encourages community-driven enhancements, fostering improvements in AI safety practices.
Insights and Results
Extensive benchmarking highlights the efficacy of Granite Guardian. On public datasets for harmful content detection, the 8B variant achieved an AUC of 0.871, outperforming baselines like Llama Guard and ShieldGemma. Its precision-recall trade-offs, represented by an AUPRC of 0.846, reflect its capability to detect harmful prompts and responses. In RAG-related evaluations, the models demonstrated strong performance, with the 8B model achieving an AUC of 0.895 in identifying groundedness issues.
The models’ ability to generalize across diverse datasets, including adversarial prompts and real-world user queries, showcases their robustness. For instance, on the ToxicChat dataset, Granite Guardian demonstrated high recall, effectively flagging harmful interactions with minimal false positives. These results indicate the suite’s ability to provide reliable and scalable risk detection solutions in practical AI deployments.
Conclusion
IBM’s Granite Guardian offers a comprehensive solution to safeguarding LLMs against risks, emphasizing safety, transparency, and adaptability. Its capacity to detect a wide range of risks, combined with open-source accessibility, makes it a valuable tool for organizations aiming to deploy AI responsibly. As LLMs continue to evolve, tools like Granite Guardian ensure that this progress is accompanied by effective safeguards. By supporting collaboration and community-driven enhancements, IBM contributes to advancing AI safety and governance, promoting a more secure AI landscape.
Check out the Paper, Granite Guardian 3.0 2B, Granite Guardian 3.0 8B and GitHub Page. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 60k+ ML SubReddit.
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