LLM benchmarks from Vellum

(written by lawrence krubner, however indented passages are often quotes). You can contact lawrence at: lawrence@krubner.com, or follow me on Twitter.

This is an area where a lot seems to be growing up quickly. Again, the question is, how do you track the accuracy and performance of your LLM? Vellum is offering a suite of tools:

Why do we need LLM benchmarks?

They provide a standardized method to evaluate LLMs across tasks like coding, reasoning, math, truthfulness, and more.

By comparing different models, benchmarks highlight their strengths and weaknesses.

Below we share more information on the current LLM benchmarks, their limits, and how various models stack up.

Model Performance Across Key LLM Benchmarks
These are the most commonly utilized LLM Benchmarks among models’ technical reports:

MMLU – Multitask accuracy

HellaSwag – Reasoning

HumanEval – Python coding tasks

BBHard – Probing models for future capabilities

GSM-8K – Grade school math

MATH – Math problems with 7 difficulty levels

In the upcoming sections, we’ll cover more info about these benchmarks, their datasets, and how to use them. But first, let’s grasp how the top 10 LLM models rank on these benchmarks

Post external references

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