GPT-4 and professional benchmarks: the wrong answer to the wrong question

We already know this is about self-driving cars. Passing a driver's test was already possible in 2015 or so, but SDCs clearly aren't ready for L5 deployment even today.

There are also a lot of excellent examples of failure modes in object detection benchmarks.

Tests, such as driver's tests or standardized exams, are designed for humans. They make a lot of entirely implicit assumptions about failure modes and gaps in knowledge that are uniquely human. Automated systems work differently. They don't fail in the same way that humans fail, and therefore need different benchmarks.

Designing good benchmarks that probe GPT systems for common failure modes and weaknesses is actually quite difficult. Much more difficult than designing or training these systems, IME.

Is it? Based on the restrictions placed on the systems we see today and the way people are breaking it, I would say that some failure modes are known.

Tests of humans have evolved over a long time and large sample size, and humans may be more similar to each other than LLMs are, so failure modes may be more universal.

But very short history, small sample size, and diversity of architecture and training means we really don’t know how to test and measure LLMs. Yes, some failure modes are known, but how many are not?