ShieldGemma: Generative AI Content Moderation Based on Gemma
- URL: http://arxiv.org/abs/2407.21772v2
- Date: Sun, 4 Aug 2024 22:13:39 GMT
- Title: ShieldGemma: Generative AI Content Moderation Based on Gemma
- Authors: Wenjun Zeng, Yuchi Liu, Ryan Mullins, Ludovic Peran, Joe Fernandez, Hamza Harkous, Karthik Narasimhan, Drew Proud, Piyush Kumar, Bhaktipriya Radharapu, Olivia Sturman, Oscar Wahltinez,
- Abstract summary: ShieldGemma is a suite of safety content moderation models built upon Gemma2.
Models provide robust, state-of-the-art predictions of safety risks across key harm types.
- Score: 49.91147965876678
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present ShieldGemma, a comprehensive suite of LLM-based safety content moderation models built upon Gemma2. These models provide robust, state-of-the-art predictions of safety risks across key harm types (sexually explicit, dangerous content, harassment, hate speech) in both user input and LLM-generated output. By evaluating on both public and internal benchmarks, we demonstrate superior performance compared to existing models, such as Llama Guard (+10.8\% AU-PRC on public benchmarks) and WildCard (+4.3\%). Additionally, we present a novel LLM-based data curation pipeline, adaptable to a variety of safety-related tasks and beyond. We have shown strong generalization performance for model trained mainly on synthetic data. By releasing ShieldGemma, we provide a valuable resource to the research community, advancing LLM safety and enabling the creation of more effective content moderation solutions for developers.
Related papers
- Internal Activation as the Polar Star for Steering Unsafe LLM Behavior [50.463399903987245]
We introduce SafeSwitch, a framework that dynamically regulates unsafe outputs by monitoring and utilizing the model's internal states.
Our empirical results show that SafeSwitch reduces harmful outputs by over 80% on safety benchmarks while maintaining strong utility.
arXiv Detail & Related papers (2025-02-03T04:23:33Z) - Internal Activation Revision: Safeguarding Vision Language Models Without Parameter Update [8.739132798784777]
Vision-language models (VLMs) demonstrate strong multimodal capabilities but have been found to be more susceptible to generating harmful content.
We propose an textbfinternal activation revision approach that efficiently revises activations during generation.
Our framework incorporates revisions at both the layer and head levels, offering control over the model's generation at varying levels of granularity.
arXiv Detail & Related papers (2025-01-24T06:17:22Z) - HarmLevelBench: Evaluating Harm-Level Compliance and the Impact of Quantization on Model Alignment [1.8843687952462742]
This paper aims to address gaps in the current literature on jailbreaking techniques and the evaluation of LLM vulnerabilities.
Our contributions include the creation of a novel dataset designed to assess the harmfulness of model outputs across multiple harm levels.
We provide a comprehensive benchmark of state-of-the-art jailbreaking attacks, specifically targeting the Vicuna 13B v1.5 model.
arXiv Detail & Related papers (2024-11-11T10:02:49Z) - Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training [67.30423823744506]
This study addresses a critical gap in safety tuning practices for Large Language Models (LLMs)
We introduce a novel approach, Decoupled Refusal Training (DeRTa), designed to empower LLMs to refuse compliance to harmful prompts at any response position.
DeRTa incorporates two novel components: (1) Maximum Likelihood Estimation with Harmful Response Prefix, which trains models to recognize and avoid unsafe content by appending a segment of harmful response to the beginning of a safe response, and (2) Reinforced Transition Optimization (RTO), which equips models with the ability to transition from potential harm to safety refusal consistently throughout the harmful
arXiv Detail & Related papers (2024-07-12T09:36:33Z) - Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning [61.2224355547598]
Open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress.
Our investigation exposes a critical oversight in this belief.
By deploying carefully designed demonstrations, our research demonstrates that base LLMs could effectively interpret and execute malicious instructions.
arXiv Detail & Related papers (2024-04-16T13:22:54Z) - AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts [0.0]
As Large Language Models (LLMs) and generative AI become more widespread, the content safety risks associated with their use also increase.
We find a notable deficiency in high-quality content safety datasets and benchmarks that comprehensively cover a wide range of critical safety areas.
To address this, we define a broad content safety risk taxonomy, comprising 13 critical risk and 9 sparse risk categories.
arXiv Detail & Related papers (2024-04-09T03:54:28Z) - Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes [0.0]
Large Language Models (LLMs) have gained widespread adoption across various domains, including chatbots and auto-task completion agents.
These models are susceptible to safety vulnerabilities such as jailbreaking, prompt injection, and privacy leakage attacks.
This study investigates the impact of these modifications on LLM safety, a critical consideration for building reliable and secure AI systems.
arXiv Detail & Related papers (2024-04-05T20:31:45Z) - RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content [62.685566387625975]
Current mitigation strategies, while effective, are not resilient under adversarial attacks.
This paper introduces Resilient Guardrails for Large Language Models (RigorLLM), a novel framework designed to efficiently moderate harmful and unsafe inputs.
arXiv Detail & Related papers (2024-03-19T07:25:02Z) - Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs [59.596335292426105]
This paper collects the first open-source dataset to evaluate safeguards in large language models.
We train several BERT-like classifiers to achieve results comparable with GPT-4 on automatic safety evaluation.
arXiv Detail & Related papers (2023-08-25T14:02:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.