The Challenge of Building Reliable AI Benchmarks 🤖

Are current AI evaluations accurately and reliably tracking AI progress? In this interview, recorded in November 2024, Epoch AI researcher Jean-Stanislas Denain (JS) explores the limitations today's benchmarks face—including saturation, lack of realism, and contamination—and discusses why these issues hinder our understanding of LLM capabilities and their real-world impact.

JS explains the importance of measuring whether AI can perform economically valuable real-world tasks, and of maintaining consistency in how evaluations are performed across models. He describes Epoch AI’s efforts to create improved, realistic benchmarks to better track and anticipate model improvements.

This conversation provides insights into current benchmarking challenges in AI and strategies researchers are implementing to reliably track future AI progress. To learn more about Epoch AI’s work on benchmarking the capabilities and AI systems, visit our Benchmarking Hub: https://epoch.ai/data/ai-benchmarking-dashboard

Timestamps

00:00 - Preview
00:30 - Why is it so important to have good AI benchmarks?
01:20 - What are the most important challenges in building good AI benchmarks?
04:04 - What's an example of benchmark saturation?
05:33 - How did saturation become a problem?
06:10 - How can AI practitioners improve benchmarks?
08:23 - Why is it so important to evaluate AI systems in a consistent way, such as in terms of the question format?
10:13 - What role does benchmarking play in things like responsible scaling policies?
11:37 - What kind of benchmarking work does Epoch AI focus on?