Why Traditional AI Evaluation Approaches Fall Short: A Look at LM Evaluators

Join me as I challenge traditional evaluation approaches and reveal why your AI system's evaluations might be meaningless, and how we can fix them to build systems that deliver in the real world.

1. The speaker is presenting at the AI Engineering Summit about LM (likely machine learning) evaluations.
2. They will discuss why evaluations might be meaningless and how to improve them.
3. The speaker co-founded Honeyhive in late 2022 to build evaluation tooling for AI engineers.
4. Honeyhive has worked with hundreds of teams across various industries, identifying patterns in evaluation problems.
5. Evaluation is crucial for building AI systems that deliver results, not just impressive demos.
6. An evaluation framework needs three key components: an agent, a dataset, and evaluators.
7. The agent can be any part of the AI system being evaluated, with unique requirements and challenges based on its function.
8. A comprehensive dataset should include both inputs (queries and requests) and ideal outputs (desired responses), covering happy paths as well as tricky edge cases.
9. Domain experts should write examples for the dataset to ensure proper business context and accurate judgments.
10. Evaluators measure quality through various methods, such as subject matter expert reviews, go-based evaluators, or LM evaluators.
11. LM evaluators combine the nuanced reasoning of human evaluators with the speed and scalability of automated systems.
12. The evaluation components (agent, dataset, evaluators) need to evolve over time as the AI system improves.
13. LM evaluators have become popular because they are cheaper, faster, and more consistent than human evaluations.
14. However, LM evaluators can suffer from criteria drift when their notion of "good" no longer aligns with the user's expectations.
15. Data set drift is another issue, as real-world user queries may not be represented accurately in the test cases.
16. To address these problems, evaluators and datasets need to be iteratively aligned like an actual AI application.
17. Align evaluators with domain experts through regular grading and critiquing of outputs.
18. Keep data sets aligned with real-world user queries by logging test banks and incorporating underperforming queries.
19. Measure and track alignment over time using concrete metrics like F1 scores or correlation coefficients.
20. Customize LM evaluator prompts, paying attention to the evaluation criteria and rating scales used.
21. Involve domain experts early in the process to ensure their judgments align with your application's goals.
22. Log production underperformance as opportunities to improve test banks and identify evaluation system weaknesses.
23. Continuously improve evaluator prompts, making them more specific to your use case, and build internal tools for domain experts to iterate on the prompts.
24. Track alignment scores over time using a simple dashboard to ensure continuous improvement in the evaluator template.
25. The goal is continuous improvement, not perfection; build iterative feedback loops into the development process for better LM evaluations.

Source: AI Engineer via YouTube

❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!