Transforming AI Evaluation Frameworks: Addressing Static Problems and Achieving Real-World Success

As AI engineers, we often overlook the importance of meaningful evaluations, but I'm here to tell you that it's crucial to fix these issues and develop iterative feedback loops to ensure your LM evaluators are aligned with real-world usage.

1. The speaker is presenting at the AI Engineering Summit about LM (likely Machine Learning) evaluations and how they might be meaningless.
2. The speaker co-founded Honeyhive in late 2022 to build evaluation tooling for AI engineers.
3. Honeyhive works with teams across the spectrum of AI, including two-person startups and Fortune 100 Enterprises.
4. Common problems with evaluation are seen across different teams, regardless of size or industry.
5. Standard testing frameworks may not be equipped to handle these specific problems in AI evaluation.
6. The goal of getting evaluation right is not just about catching bugs or measuring accuracy, but building AI systems that deliver results in the real world.
7. An evaluation involves three key components: an agent, a dataset, and evaluators.
8. The agent is whatever is being evaluated, which could be an end-to-end system or a small function within a larger system.
9. Each agent has unique requirements and challenges that must be accounted for in the evaluation process.
10. A dataset is crucial for a good evaluation framework, as it provides the basis for comparison and testing.
11. Datasets should include both inputs (queries or requests) and ideal outputs (what good responses should look like).
12. Datasets need to cover not just the happy path but also tricky edge cases where things might go wrong.
13. Evaluators measure quality, which can be done through subject matter experts reviewing outputs, using automated systems, or a combination of both.
14. LM evaluators promise to combine the best of both worlds by providing nuanced reasoning and understanding of human context with the speed and scalability of automated systems.
15. The three components of evaluation (agent, dataset, evaluators) need to evolve over time as the agent improves.
16. LM evaluators have become popular due to their speed, cost reduction, and consistency with human judgments.
17. However, LM evaluators also face challenges such as criteria drift and data set drift.
18. Criteria drift occurs when an evaluator's notion of what is good no longer aligns with the user's notion of good.
19. Data set drift happens when datasets lack test coverage, causing them to not represent real-world user queries accurately.
20. To fix these problems, evaluators and datasets need to be iteratively aligned like how actual ML applications are aligned.
21. This can be achieved through a three-step approach: aligning evaluators with domain experts, keeping data sets aligned with real-world user queries, and measuring and tracking alignment over time
22. Customizing the LM evaluator prompt is essential to ensure that it measures something meaningful instead of relying on out-of-the-box metrics.
23. Involving domain experts early in the process and continuously improving the evaluator prompt is key to ensuring alignment with real-world usage.
24. Building iterative feedback loops into the development process is crucial for continuous improvement, rather than treating tests like static tests in traditional software development.

Source: AI Engineer via YouTube

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