Model Evaluation (Evaluation)

Why Do We Evaluate?

The AI world is buzzing. Several top models have released "blockbusters" almost simultaneously. Everyone at their launch events shows off complicated charts, all claiming to be number one in the world.

But here's the question: "How exactly did they compare? Who is genuinely strong, and who is just bragging?"

Without understanding LLM evaluation, large models remain impenetrable black boxes.

Four Purposes of Evaluation

1. Compare Core Capabilities

Understand the relative strengths of different models across dimensions like knowledge, reasoning, and coding.

2. Guide User Choices

With hundreds of models on the market, quantified scores determine where the money goes.

3. Assess Training Results

After parameter changes or new data input, did the model get smarter or dumber? Evaluation prevents groping in the dark.

4. Guide Technical Iteration

Identify weaknesses in logical reasoning or coding ability to clarify the direction of future optimization.

What Should Be Evaluated?

Basic Capabilities

• Language understanding
• Knowledge reserves
• Translation ability

Reasoning

• Can it solve math problems correctly
• Can it spot logical traps

Vertical Capabilities

• Writing code
• Reading medical reports
• Drafting legal documents

Application & Alignment

• Following instructions
• Tool calling
• These determine real-world usefulness

Safety & Alignment

• Refusing harmful content
• Avoiding hallucination
• Preventing bias

Comparison of Evaluation Methods

| Method | Pros | Cons | Best For | |--------|------|------|----------| | Fixed-answer evaluation | Fast, low cost | Inflexible, can't judge open questions | Standardized testing | | LLM-as-Judge | Handles long text, highly automated | Contains biases, the judge itself may be a bottleneck | Subjective questions | | Human preference | Closest to user experience, highest quality | Extremely slow, extremely expensive, subjective | Final validation |

Core Benchmarks

MMLU — General Academic Knowledge

• 57 subjects: from elementary math to US history, law, and medicine
• Multiple choice: pick 1 of 4, one point for a correct answer
• Current state: top models essentially score full marks

MMLU-Pro — Harder Version

• More options: 4 choices → 10 choices (lucky guessing is nearly impossible)
• Harder questions: easy points removed, complex reasoning added
• Chain-of-Thought (CoT): models must show their reasoning, not just an answer

GSM8K — Math & Logic

Tests mathematical reasoning, like asking an AI to solve elementary school competition math problems to check its calculation accuracy.

HumanEval — Coding

Tests code generation: given a function signature, the AI must complete the rest of the implementation.

GPQA Diamond — Graduate Level

• Google-Proof: even copying the question into Google won't find a direct answer
• Requires real understanding: you must grasp the principles and derive the answer
• Current status: Gemini 3.0 & GPT 5.2 have surpassed the average human expert

HLE (Humanity's Last Exam) — Humanity's Last Line of Defense

> "If an AI can score full marks on this exam, we can basically say humanity can no longer hold AI back."
• Questions written by 1,000+ top experts from 50+ countries
• Extremely difficult: complex engineering drawings, ambiguous medical images, deciphering ancient scripts
• Current top models perform very poorly on it

Summary

Model evaluation is a cornerstone of AI development. Through standardized benchmarks, we can objectively compare the capabilities of different models, guide technical iteration, and help users make informed choices. The continuous upgrading of benchmarks — from MMLU to HLE — is pushing AI models toward ever higher levels.

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*Source: Easy AI Tutorial*