Are AI Benchmarks Misleading Us? Here’s Why Real-World Performance Matters More

AI and Large Language Models (LLMs) have been all the rage for 18 months now, popping up in headlines and catching the attention of millions. When people talk about these AI models, they often compare them based on their size, how well they do on certain benchmarks like the MMLU (Massive Multi-Task Language Understanding), and even their performance on math tests. But here's the thing: these measures might not actually tell us how good an AI is in real-world scenarios.

Size Doesn’t Always Matter

Bigger isn’t always better. You might think a larger model would naturally outperform a smaller one, but that's not necessarily true. Sometimes, smaller models that are trained for specific tasks can actually do a better job. It’s like having a Swiss Army knife versus a specialised tool. The Swiss Army knife (the big model) has lots of functions, but if you need to fix a watch, the specialised tool (the small model) is your best bet.

Quality Over Quantity

Another crucial point is the quality of the data these models are trained on. A model trained on high-quality, relevant data will likely perform better than a model trained on a large amount of mediocre data. Imagine trying to learn a subject from an expert versus reading a ton of random blog posts; the expert will get you there faster and more reliably.

The Problem with Benchmarks

Benchmarks like the MMLU can give us some idea of how an AI performs, but they have their limitations. Models can be trained to ace these tests without really understanding the material. It’s like a student who memorises answers for an exam but doesn’t actually grasp the concepts. So, while a model might score high on a benchmark, it doesn’t necessarily mean it will be effective in practical applications.

Math Tests: Not a Great Fit

Then there’s the issue of using math tests to gauge an AI's logic. Sure, math requires logical thinking, but LLMs are primarily built to understand and generate language. Testing them on math can be a bit like judging a fish by its ability to climb a tree. It just doesn’t make much sense and doesn't play to their strengths.

A More Meaningful Measure

Ultimately, what matters most is how effective an AI is at doing what it’s supposed to do. This could be helping customer service reps answer questions, generating creative content, or any number of other tasks. Focusing on real-world performance and practical applications gives us a better sense of an AI’s true capabilities.

If you’re interested in reading more about why these tests might not be the best measure of AI performance, check out this article from The Markup .