LLM Benchmarks - The Olympics of AI (but with fewer medals and more math)

If you’ve ever wondered how people figure out which Large Language Model (LLM) is “the best,” welcome to the world of LLM benchmarks. Think of them as the fitness tests of the AI world. They put models through their paces with challenges like writing code, solving math problems, or even understanding jokes. (Yes, there’s a test for that too. No, not all LLMs pass.)

But here’s the thing: LLMs are constantly competing, leapfrogging each other on these benchmarks faster than your favorite streaming service updates its content library. One day, Model X is the king of the leaderboard; the next, Model Y shows up with a crown and a mic drop.

General vs. Specialized LLMs

Not all LLMs are built the same. Some aim to be jack-of-all-trades models (general LLMs), excelling at a wide range of tasks like conversation, coding, or reasoning. Think of these as the Swiss Army knives of AI:

• OpenAI GPT-4o
• Anthropic Claude 3.5 Sonnet
• Google Gemini 2.0
• Meta Lllama 3.1

Others are specialists, fine-tuned for a specific purpose, like diagnosing diseases, analyzing financial documents, or interpreting legal contracts. Examples include:

• Medical Models: Google’s MedPaLM, fine-tuned for healthcare applications like diagnosing symptoms or explaining medical guidelines.
• Financial Models: BloombergGPT, designed to understand financial jargon and analyze market data
• Legal Models: Casetext’s CoCounsel, trained to assist lawyers by summarizing legal briefs and identifying case precedents.

To help you make sense of this AI rivalry, I’ve put together a cheat sheet of popular LLM benchmarks. It’s simple, non-technical, and guaranteed not to induce flashbacks to high school exams. 😅

So, which type does this cheat sheet measure?

• Most benchmarks listed here are designed to evaluate general LLMs, testing their ability to perform across a range of topics and tasks.
• However, benchmarks like HumanEval, MMLU, or domain-specific datasets are particularly useful for evaluating specialized LLMs.

By understanding which type of model you’re evaluating, you can choose the benchmarks that best align with your needs.

🏅 LLM Benchmark Cheat Sheet

BIG-bench (Beyond the Imitation Game Benchmark)

• What it tests: EVERYTHING—science, math, reasoning, even humor.
• Example: Generate a joke that combines physics and cooking.
• Leaderboard: HuggingFace Open LLM Leaderboard - look at the BBH column.
• Author: Google Research and collaborators.
• Citation: “Beyond the Imitation Game Benchmark (BIG-Bench),” Srivastava et al., 2022.

MTEB (Massive Text Embedding Benchmark)

• What it tests: Text similarity and understanding.
• Example: Identify if the sentences “The cat sat on the mat” and “The feline rested on the rug” mean the same thing.
• Leaderboard: HuggingFace MTEB Leaderboard.
• Author: Hugging Face.
• Citation: “Massive Text Embedding Benchmark (MTEB),” Muennighoff et al., 2023.

Chatbot Arena

• What it tests: Head-to-head conversational battles between LLMs.
• Example: Debate the pros and cons of electric vehicles versus gasoline cars.
• Leaderboard: Chatbot Arena Leaderboard.
• Author: LMSYS Organization.
• Citation: “Chatbot Arena: Comparing Language Models,” LMSYS, 2023.

HellaSwag

• What it tests: Commonsense reasoning.
• Example: You are baking cookies, and the timer goes off. What do you do next? Options: A) Take the cookies out of the oven, B) Go for a run, C) Start a new batch of cookies. Answer: A) Take the cookies out of the oven.
• Leaderboard: Papers With Code - HellaSwag Leaderboard.
• Author: Allen Institute for AI.
• Citation: “HellaSwag: Can a Machine Really Finish Your Sentence?” Zellers et al., 2019.

TruthfulQA

• What it tests: The model’s ability to generate truthful and informative answers.
• Example: Question: Can humans breathe in outer space without assistance? Answer: No, humans need a spacesuit or oxygen supply.
• Leaderboard: TruthfulQA Leaderboard.
• Author: OpenAI.
• Citation: “TruthfulQA: Measuring How Models Mimic Human Falsehoods,” Lin et al., 2021.

MMLU (Massive Multitask Language Understanding)

• What it tests: Knowledge across diverse domains.
• Example: What is the capital of Australia? Answer: Canberra.
• Leaderboard: Papers with Code - MMLU Leaderboard.
• Author: OpenAI.
• Citation: “Evaluating Large Language Models Trained on Code,” Hendrycks et al., 2021.

HumanEval

• What it tests: Coding skills. Can the LLM write functional code snippets based on a problem description?
• Example: Write a Python function to calculate the factorial of a number.
• Leaderboard: HuggingFace Big Code Models Leaderboard
• Author: OpenAI
• Citation: “OpenAI Codex: Generating Code from Natural Language,” OpenAI, 2021.

MMLU-Pro (Massive Multitask Language Understanding Pro)

• What it tests: Advanced reasoning across specialized and general knowledge domains.
• Example: What is the primary function of red blood cells in humans? Options: A) Transport oxygen, B) Fight infections, C) Store energy, D) Break down toxins. Answer: A) Transport oxygen.
• Leaderboard: HuggingFace MMLU-Pro Leaderboard.
• Author: OpenAI and collaborators.
• Citation: “MMLU-Pro: Advancing Reasoning Benchmarks for LLMs,” Hendrycks et al., 2023.

Why This Matters For businesses: Benchmarks can help you pick the right LLM for your specific needs. For everyone else: They give us a way to understand what these AI tools can (and can’t) do yet.

Pro Tip: Benchmarks are great, but they’re not the whole story. Think of them like movie reviews—useful, but your experience might vary.

So, which LLM will win the gold medal next? Stay tuned—it’s like binge-watching a tech drama, but the plot twists are all made of math and code. 🎉