Responsible generative AI: Evaluation best practices and tools (AIM342)

Evaluating Responsible Generative AI Models

Key Takeaways

  1. The Need for Evaluation: Generative AI models, such as large language models (LLMs), are incredibly powerful but also come with new risks that need to be carefully evaluated. This is crucial for the responsible adoption of these technologies.

  2. Evaluation Dimensions: Four key dimensions to evaluate are:

    • Quality/Accuracy: How well does the model perform the intended task?
    • Latency: How long does it take for the model to generate the output?
    • Cost: What are the costs associated with running the model?
    • Confidence: How confident can we be that the model will behave as expected and not cause harm?
  3. Evaluation Approaches:

    • Human evaluation: Using human raters to assess the model outputs.
    • Automatic evaluation: Using heuristic metrics and LLM-based judges to automatically evaluate the outputs.
    • Combination of approaches: Using both human and automatic evaluation for comprehensive assessment.
  4. Responsible AI Dimensions:

    • Veracity and Robustness: Ensuring the model outputs are truthful, relevant, and coherent.
    • Privacy and Security: Preventing the leakage of private information.
    • Safety: Ensuring the model does not generate harmful or unsafe content.
    • Fairness: Mitigating demographic biases in the model's performance.
  5. Establishing Launch Confidence:

    • Define the use case narrowly to identify relevant risks.
    • Choose appropriate metrics to measure the responsible AI dimensions.
    • Set release criteria based on the severity and likelihood of risks.
    • Design representative evaluation datasets and use statistical methods to quantify uncertainty.
    • Implement mitigation strategies (e.g., filtering) if confidence is low.
    • Continuously monitor and re-evaluate the model during production.
  6. Tools and Resources:

    • AWS Bedrock for model evaluation, including LLM-based judges.
    • AWS SageMaker and open-source tools for automatic evaluation.
    • AWS AI Service Cards for transparent reporting of model performance and limitations.

The key message is that evaluating generative AI models, especially for responsible production use, requires a comprehensive, multifaceted approach that considers both technical and ethical dimensions. Establishing launch confidence is crucial, and this involves a structured process of risk assessment, metric selection, and statistical analysis.

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