Benchmarks for artificial intelligence are standardized tests that developers use to evaluate the performance, accuracy and other characteristics of AI models across different tasks, as well as to compare them.
Benchmarks play an important role in determining how well a model handles certain types of tasks, be it text processing, image recognition or complex mathematical calculations. As of 2024 there are many well-known benchmarks used for various AI systems.
Main types of benchmarks:
MLPerf
One of the best-known and most widespread benchmarks for evaluating machine learning performance. MLPerf uses tests that cover several tasks, including model training and inference, both in the cloud and on edge devices. An example model from this benchmark is Llama 2 70B, which is used to evaluate the performance of large language models (LLMs) in real-world application scenarios.
HellaSwag
This benchmark is designed to evaluate common sense in language models. Models such as GPT-4 show high accuracy on this benchmark, demonstrating how well they can pick the right options in the situations presented. The task is for the model to complete sentences using common sense.
MMLU (Massive Multitask Language Understanding)
A benchmark for evaluating the multitasking ability of language models. It includes tasks in mathematics, history, law and other disciplines, testing how well a model can handle different subject areas. Models such as GPT and LLAMA often use this benchmark to demonstrate the breadth of their knowledge.
GSM8K
A specialized benchmark for mathematical problems, based on solving simple arithmetic operations. It tests how models can solve problems that require several steps of logical reasoning, and is used to evaluate the mathematical abilities of AI.
TruthfulQA
A benchmark for evaluating the truthfulness of a model's answers. It measures how well a model can answer questions while avoiding the spread of false or unverified information. This test is especially relevant for applications where it is important to provide reliable data (for example, medicine or law).
The advantages and importance of benchmarks
Benchmarks make it possible to standardize the evaluation of AI models, making results comparable across different systems. They help you:
- Evaluate the performance of models on specific tasks, such as pattern recognition, text analysis or code generation.
- Improve the accuracy of models through the ability to test on complex datasets, including multitask and multi-parameter tests.
- Understand the limitations of models — many benchmarks show which types of tasks models handle better or worse.
For example, a score on a mathematical reasoning task can differ greatly from a score on a general text analysis task.