LLM Benchmarks (HELM, MMLU, TruthfulQA)

How Do We Know Which AI Model Is "Better"?

With so many Large Language Models (LLMs) available — GPT-4, Claude, Gemini, Mistral, LLaMA, and more — how do we compare them fairly?

That’s where benchmarks come in. Benchmarks are standardized tests used to evaluate how well a model performs on tasks like reasoning, knowledge, truthfulness, and more.

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🧠 Why Are Benchmarks Important?

• Help developers choose the right model for their use case

• Show strengths and weaknesses (e.g., factual accuracy vs reasoning)

• Provide a common language to compare LLMs across vendors

• Ensure models meet minimum quality and safety standards

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🧪 Key LLM Benchmarks to Know

1. HELM (Holistic Evaluation of Language Models)

📌 Created by Stanford CRFM

Focus Areas	Details
Comprehensiveness	Evaluates multiple dimensions like accuracy, robustness, bias, and toxicity
Multi-task, Multi-model	Tests many LLMs across many tasks
Transparent	Public results with open-source methodology

✅ Great for overall model comparison across safety, bias, and robustness.

🔗 https://crfm.stanford.edu/helm/latest/

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2. MMLU (Massive Multitask Language Understanding)

📌 Created by OpenAI researchers

Focus Areas	Details
Knowledge + Reasoning	Tests across 57 subjects (math, history, medicine, law)
Multiple choice format	Measures performance on academic-style tasks
Human-level benchmark	GPT-4 surpasses average human on this

✅ Best for academic reasoning, factual depth, and domain knowledge.

🔗 https://github.com/hendrycks/test

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3. TruthfulQA

📌 Created by OpenAI & partners

Focus Areas	Details
Truthfulness	Measures whether models hallucinate or give false answers
Adversarial questions	Designed to trick LLMs into repeating false info
High difficulty	Most models fail without prompt tuning

✅ Critical for evaluating hallucination risk and factual safety.

🔗 https://truthfulqa.github.io

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📈 Bonus Benchmarks

Name	Focus Area
BIG-bench	General capabilities across weird, creative, and niche tasks
ARC	Scientific and commonsense reasoning
GSM8K	Grade-school math problems
HumanEval	Code generation and correctness
MT-Bench	Multilingual & instruction-following capabilities

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🧠 Summary

• HELM = Big-picture benchmark for robustness, bias, and safety

• MMLU = Deep knowledge and reasoning test across academic subjects

• TruthfulQA = Checks if the model tells the truth under pressure

Together, these benchmarks help you:

• Pick the right model

• Tune for safety

• Understand model trade-offs (e.g., smarter vs safer)

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