The Ultimate Guide to LLM Benchmarks: Evaluating Language Model Capabilities

As large language models (LLMs) rapidly advance, benchmarking their capabilities has become essential for assessing progress and guiding future research. A diverse array of benchmarks has emerged, each designed to evaluate specific facets of language understanding and generation, spanning domains such as commonsense reasoning, mathematical problem-solving, code generation, and question-answering.

We analyzed the most popular open- and closed-source LLMs to devise a comprehensive list of the most widely used benchmarks for evaluating state-of-the-art LLMs.

Some of the most popular LLMs and their papers, according to the LMSYS Chatbot Arena Leaderboard, a crowdsourced open platform with over 500,000 human preference votes:

• Claude 3: Technical Report

• Gemini 1.5: Technical Report 

• GPT-4: Technical Report 

• Command R+: Blog Post

• Qwen1.5: Blog Post

• Mistral Large: Blog Post

• Mixtral 8x7B: Blog Post

• Llama 2: Technical Report

Now, to the main list of the benchmarks! →

Commonsense Reasoning

HellaSwag

• Objective: Test how well an LLM can understand and apply everyday knowledge to logically complete scenarios.

• Format: Multiple choice situation -> predict likely continuation

• Challenge: Difficult for LLMs but easy for humans (>95% accuracy) due to the need for real-world knowledge and logical reasoning.

• Original paper: HellaSwag: Can a Machine Really Finish Your Sentence?