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.

Therefore standardized tests are probably "easy mode" for GPT, and we shouldn't over-generalize its performance there to its ability to actually add economic value in actually economically useful jobs. Fine-tuning is maybe a possibility, but its expensive and fragile, and I don't think its likely that every single job is going to get a fine-tuned version of GPT.

Fine tuning should not be used to attempt to impart knowledge that didn't exist in the original training set, as it is just the wrong tool for the job.

Knowledge graphs and vector similarity search seem like the way forward for building a corpus of information that we can search and include within the context window for the specific question a user is asking without changing the model at all. It can also allow keeping only relevant information within the context window when the user wants to change the immediate task/goal.

Edit: You could think of it a little bit like the LLM as an analog to the CPU in a Von Neumann architecture and the external knowledge graph or vector database as RAM/Disk. You don't expect the CPU to be able to hold all the context necessary to complete every task your computer does; it just needs enough to store the complete context of the task it is working on right now.