LLM Content Safety Evaluation using ShieldGemma
Introduction
ShieldGemma is a suite of instruction-tuned, open-weight content classification models, based on the Gemma 2 architecture, designed to evaluate user-provided, model-generated, or hybrid content for potential violations of content safety policies. These models leverage instruction tuning to enhance their accuracy in identifying harmful, inappropriate, or policy-violating content generated by LLM, offering a scalable and adaptable solution for content moderation and safety assurance.
Despite prior safety tuning and the implementation of a well-structured prompt template, AI models may still generate content that leads to unintended negative consequences. Therefore, incorporating models that perform comprehensive safety evaluations is crucial within the LLM pipeline to mitigate risks and ensure responsible outputs.
- ShieldGemma models are designed to detect four specific categories of harmful content including Harassment, Hate Speech, and Dangerous Content.
- These models are available in three size-class variants: 2 billion, 9 billion, and 27 billion parameters .
- Each ShieldGemma model is specialized to classify a single harm category at a time, requiring separate calls to assess different types of harm. This focused approach can lead to improved accuracy, particularly when using the smaller 2B parameter model.
Operating Modes:
1. Prompt-only Mode (Input Filtering): In this mode, ShieldGemma analyzes user query/content to determine whether it violates a content safety policy either directly or by attempting to provoke harmful responses from the AI model.
2. Prompt-response Mode (Output Filtering): Here, both the user input and the model’s generated response are analyzed, with ShieldGemma predicting whether the output generated by LLM violates the relevant content safety guidelines.
Prediction Modes:
1. Scoring Prediction Mode: ShieldGemma operates most effectively in this mode, where it assigns a score between 0 and 1. Scores closer to 1 indicate a higher likelihood of content violation. This mode allows for fine-tuning the filtering threshold, offering precise control over the model’s sensitivity to harmful content.
2. Generating Mode: ShieldGemma can also function in a generation mode, Where it provides a textual output Yes/No and associated reasons of why content policy has been violated. However, this mode offers less transparency and control compared to the scoring mode, making it less suitable for situations requiring granular oversight of content filtering.
ShieldGemma in prompt-only prediction mode
ShieldGemaa in genrative prompt-response mode
Takeaways
ShieldGemma introduces a new paradigm in LLM safety by offering customizable, instruction-tuned models for content classification, trained on a few tens of thousands of examples.
Although ShieldGemma specializes in a limited set of harm categories, users can define custom categories in any language by training with just a few thousand labeled examples or by using prompt engineering , making it adaptable to specific content moderation needs.
You can find the code samples related to ShieldGemma in the google Colab notebook
You can find the complete details on ShieldGemma in the publication.
Future Topics to Explore:
1. Detailed Code Walkthrough for ShieldGemma: A deeper dive into the code, demonstrating how to implement and use ShieldGemma models.
2. Performance Benchmarking: Assessing ShieldGemma’s accuracy, speed, and reliability in various use cases.
3. Custom Safety Policies: Exploring how to leverage prompt engineering to create and enforce custom safety policies.
4. Fine-Tuning for Custom Policies: Techniques for adapting ShieldGemma to specific safety guidelines using fine-tuning.
5. Comparison with Other LLMs: A comprehensive evaluation of how ShieldGemma stacks up against other LLM safety evaluation models.
6. Multilingual Fine-Tuning: How to fine-tune ShieldGemma for safety evaluations across multiple languages.
If you found this blog post helpful, please clap, comment, follow, and subscribe.
