Model Evaluation & Benchmarks

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Evaluating an LLM's capabilities requires diverse benchmarks across multiple dimensions:

Metric Type	Measures	Examples
Intrinsic	Next-token prediction accuracy	Perplexity, Loss
Academic	Standard NLP task performance	GLUE, SuperGLUE
Knowledge	Factual recall and accuracy	TruthfulQA, MMLU
Reasoning	Logical and mathematical abilities	GSM8K, MATH, BBH
Human-like	Natural language generation quality	Human evaluations, Turing tests

A model's intrinsic metrics (like perplexity) don't always correlate directly with its performance on downstream tasks.

Tests knowledge across 57 subjects including STEM, humanities, law, and more

Example: "What is the Carnot efficiency of a heat engine operating between 300K and 200K?"

8,000 grade school math word problems requiring multi-step reasoning

Example: "John has 5 apples. He buys 2 more. He gives 3 to his friend. How many apples does John have now?"

164 programming problems to test code generation capabilities

Example: "Write a function to find the longest common prefix string amongst an array of strings."

Measures model tendency to reproduce falsehoods commonly believed by humans

Example: "What happens if you feed a wild bear in a national park?"

Model performance increases with scale, but with diminishing returns in some areas

Strong performance on benchmarks doesn't always translate to real-world capability.

Top models approach perfect performance on older benchmarks, necessitating creation of harder tests

Models may have seen benchmark data during pre-training, invalidating test results

Performance varies dramatically based on how questions are phrased

Many qualities (helpfulness, creativity) require subjective human judgment

Some capabilities only emerge at certain scale thresholds rather than improving gradually

The ultimate measure of an LLM's value is not benchmark scores but its ability to solve real human problems and add value in practical applications.