LLM Benchmarks in 2024.

Author(s): Tim Cvetko

Originally published on Towards AI.

An Overview of Why LLM Benchmarks Exist, How They Work, and What’s Next

LLMs are complex. Although most of us used ChatGPT to …

Write me a 100-word paragraph about the history of Greek poetry.

or

give me a dirty joke about old people

Image generated with Stable Diffusion. Obviously, we’re not there yet.

LLMs have increasingly specific and generalistic capabilities that spawn across language understanding, memorization, and maths. As these LLMs adopt ever-greater size, their performance starts to ensue into “what it means to be human”, i.e. their reasoning capabilities.

Who is this article useful for? AI Engineers, Founders, VCs, etc.

How advanced is this post? Anybody remotely acquainted with LLM should be able to follow along.

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Traditional metrics, like accuracy and F1 score, fall short of capturing the complexities of evaluating Large Language Models (LLMs). LLMs deal with intricate language tasks that are generative and random at their core. Success depends on a nuanced understanding of context, semantics, and pragmatics.

How do we measure an LLM’s model performance? To measure and compare LLM holistically, you can make use of benchmarks that have been established to test models’ performances across multiple specific reasoning tasks.

Benchmarks provide a standardized way to evaluate and improve LLMs, highlighting their strengths and weaknesses in different language tasks.

Benchmarks, such as GLUE,… Read the full blog for free on Medium.

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Published via Towards AI