Artificial Intelligence (AI)

With the announcement of new models and their impressive benchmark performance, it's important to provide some context around AI models and their benchmarks.

The issue with benchmarks is that they can become the goal in and of themselves. While they serve as a useful proxy for evaluating model performance, they don't necessarily reflect how well a model performs in real-world use cases. Benchmarks measure how well large language models (LLMs) perform in specific scenarios, but this doesn't always translate directly to broader, practical applications.

30% drop in accuracy on Putnam problems when the problems are slightly varied:

30% drop in accuracy on Putnam problems when the problems are slightly varied: https://openreview.net/forum?id=YXnwlZe0yf&noteId=yrsGpHd0Sf

🇧🇷🇺🇸-  Book: Making Things Think: https://holloway.com/mtt. Investor in Wander, Carry, Footprint, Merkle Manufactory (Farcaster), Dynamic, Paragraph.

I think they are "overfitting" to benchmarks. I'm not sure if they are becoming less dynamic though

Does this mean that models are “overfitting” to benchmarks and becoming less dynamic / capable of solving outside of the benchmarks now?