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Giuliano Giacaglia
@giu
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.
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Giuliano Giacaglia
@giu
In some ways, these metrics risk becoming vanity metrics—an example of Goodhart's Law in action: “When a measure becomes a target, it ceases to be a good measure.” A fitting example of this phenomenon comes from Google Books. The Google Books team once said, "All OCR datasets have been solved, but OCR itself has not been solved." This highlights how solving specific benchmarks doesn't always equate to solving the larger problem. As we see new and better models being developed, it's important not to get too caught up in benchmark scores alone.
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