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Evaluating LLMs

Author(s): Louis-François Bouchard

Originally published on Towards AI.

What, why, when, and how…

We always see LLMs beating all benchmarks, like the recent mysterious GPT-2 chatbot beating all models, which was actually GPT-4o. You may have heard similar claims about some models outperforming others in popular benchmarks like those on the HuggingFace leaderboard, where models are evaluated across various tasks, but how can we determine which LLM is superior exactly? Isn’t it just generating words and ideas? How can we know one is better than the other?

Let’s answer that. I’m Louis-François, co-founder of Towards AI and today, we dive into how we can accurately quantify and evaluate the performance of these models, understand the current methodologies used for this, and discuss why this process is vital.

Let’s get started.

is crucial to identifying potential risks, analyzing how these models interact with humans, determining their capabilities and limitations for specific tasks, and ensuring that their training progresses effectively. And, most importantly, it’s vital if you want to know if you are the best!

Sounds good, evaluation is useful. But what exactly are we assessing in an LLM?

When using an LLM, we expect two things from the model:

First, it completes the assigned task, whether it is summarization, sentiment analysis, question answering or anything else LLMs can do.Second,… Read the full blog for free on Medium.

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Published via Towards AI

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