LLM Benchmarks & What They Mean🔥 · The 4 Hour AI Workweek

Clintin Lyle Kruger

Jul 25 in 🧑🏻‍💻 CO-WORK SPACE

LLM Benchmarks & What They Mean🔥

We all hear about benchmarks these days

And that LLMs are measured against them

But what exactly are these benchmarks?

Here's an explainer:

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1. HumanEval and MultiPL-E

HumanEval and MultiPL-E are benchmarks used to evaluate the performance of AI models in generating and understanding code.

These benchmarks are designed to assess how well a model can complete programming tasks, understand code syntax, and generate accurate code solutions.

HumanEval:

Purpose: Measures the ability of AI models to generate correct code solutions for given programming problems.

Tasks: Typically involves coding challenges where the model must write functional code snippets based on a problem description.

Performance Indicator: Higher scores indicate better understanding and generation of code, reflecting the model’s capability in code-related tasks.

MultiPL-E:

Purpose: Evaluates the model's performance across multiple programming languages.

Tasks: Similar to HumanEval but expanded to include various programming languages, testing the model's versatility and multilingual coding proficiency.

Performance Indicator: Outperforming Llama 3.1 405B instruct and scoring just below GPT-4o suggests that Mistral Large 2 is highly proficient in coding tasks and can handle multiple languages effectively.

2. MATH (0-shot, without CoT)

MATH (0-shot, without CoT) is a benchmark designed to assess the mathematical reasoning and problem-solving abilities of AI models without prior contextual examples (0-shot) and without chain-of-thought (CoT) prompting.

Purpose: Tests the model's inherent ability to solve mathematical problems without additional hints or step-by-step guidance.

Tasks: Includes a variety of math problems ranging from basic arithmetic to more complex mathematical reasoning.

Performance Indicator: Falling only behind GPT-4o indicates that Mistral Large 2 has strong mathematical reasoning abilities, capable of solving problems independently without guided thinking processes.

3. Multilingual MMLU

Multilingual MMLU (Massive Multilingual Language Understanding) is a benchmark that measures the performance of AI models across different languages, evaluating their understanding and generation capabilities in a multilingual context.

Purpose: Assesses the model’s ability to understand and generate text in various languages, testing its multilingual proficiency.

Tasks: Includes a diverse set of language tasks such as translation, comprehension, and language-specific problem-solving in multiple languages.

Performance indicator based on the latest Mistral Large 2.

Note: There are many more benchmarks not mentioned here. This is a good starting point.

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