Comprehensive Evaluation Benchmarks
Master the complete landscape of LLM evaluation: from PIQA to MMLU to human preference benchmarks
What is Comprehensive LLM Evaluation?
Comprehensive LLM evaluation is the systematic assessment of language models across multiple cognitive and reasoning dimensions. Rather than relying on a single metric, it combines diverse benchmarks that test common sense reasoning (PIQA, HellaSwag), world knowledge (TriviaQA, NaturalQuestions), mathematical reasoning (MATH, GSM8K), code generation (HumanEval, MBPP), and composite understanding (MMLU, AGIEval).
This multi-faceted approach reveals model strengths and weaknesses across different cognitive tasks, ensuring robust evaluation that aligns with real-world performance and helps guide model selection for specific applications.
Complete Evaluation Framework
Common Sense Reasoning
- PIQA: Physical interaction reasoning
- SIQA: Social interaction QA
- HellaSwag: Commonsense NLI
- WinoGrande: Pronoun resolution
- OpenBookQA: Multi-step reasoning
- CommonsenseQA: General knowledge
World Knowledge
- TriviaQA: Trivia questions
- Natural Questions: Real queries
- SQuAD: Reading comprehension
- QuAC: Question answering in context
- BoolQ: Yes/no questions
- MS MARCO: Machine reading
Mathematical Reasoning
- MATH: Competition mathematics
- GSM8K: Grade school math
- MathQA: Math word problems
- ASDiv: Arithmetic reasoning
- SVAMP: Math word variations
- DROP: Discrete reasoning
Code Generation
- HumanEval: Python programming
- MBPP: Basic Python programs
- CodeXGLUE: Multiple code tasks
- APPS: Programming problems
- CodeContest: Competition coding
- DS-1000: Data science tasks
Composite Benchmarks
- MMLU: 57 academic subjects
- MMMU: Multimodal understanding
- AGIEval: Human-centric exams
- Big-Bench: 200+ diverse tasks
- HELM: Holistic evaluation
- Llama Eval: Meta's suite
Human Evaluation
- LMSYS Arena: Chatbot battles
- AlpacaEval: Instruction following
- MT-Bench: Multi-turn conversations
- WildBench: Real-world queries
- Arena-Hard: Challenging prompts
- Chatbot Arena: Live human voting
Benchmark Selection Strategy
How to Choose the Right Benchmarks
By Application Domain:
- Chatbots: LMSYS Arena + MT-Bench + AlpacaEval
- Educational AI: MMLU + MATH + GSM8K + SQuAD
- Code Assistants: HumanEval + MBPP + DS-1000
- Research Tools: TriviaQA + Natural Questions + Big-Bench
- General AI: MMLU + HellaSwag + PIQA + LMSYS Arena
By Model Capability:
- Basic Models (<7B): PIQA + HellaSwag + GSM8K
- Medium Models (7-30B): + MMLU + HumanEval + TriviaQA
- Large Models (30B+): + MATH + AGIEval + Arena
- Multimodal: + MMMU + VQA + Visual reasoning
- Code-focused: + CodeXGLUE + APPS + CodeContest
Key Benchmark Deep Dive
MMLU (Massive Multitask Language Understanding)
Covers STEM, humanities, social sciences. Random guessing baseline: 25%. Human expert performance: ~89%. GPT-4 achieves ~86%, while most open models score 40-60%.
MATH (Mathematical Reasoning)
High school competition level math requiring step-by-step reasoning. Most models score <10%. GPT-4 achieves ~42%, highlighting the challenge of mathematical reasoning.
HumanEval (Code Generation)
Functional correctness measured by unit tests. CodeX achieves 72%, GPT-4 ~67%. Tests both understanding of requirements and correct implementation.
LMSYS Chatbot Arena (Human Preference)
Real human preferences on diverse prompts. GPT-4 consistently ranks highest (~1200 ELO), followed by Claude and other frontier models. Correlates well with user satisfaction.
Production Evaluation Pipeline
Automated Evaluation
- • Parallel benchmark execution
- • Consistent scoring metrics
- • Reproducible results
- • Cost-effective at scale
- • Rapid iteration cycles
Human Evaluation
- • Real-world relevance
- • Subjective quality assessment
- • Edge case detection
- • User preference alignment
- • Final validation step
Limitations & Best Practices
Known Limitations
- ⚠ Data contamination in training sets
- ⚠ Limited coverage of real-world tasks
- ⚠ Cultural and demographic biases
- ⚠ Gaming through benchmark-specific tuning
- ⚠ Static benchmarks vs. evolving capabilities
- ⚠ Multiple choice vs. open-ended generation
- ⚠ English-centric evaluation
Best Practices
- ✓ Use multiple diverse benchmarks
- ✓ Include domain-specific evaluations
- ✓ Combine automated + human evaluation
- ✓ Test on held-out evaluation sets
- ✓ Monitor for benchmark contamination
- ✓ Regular benchmark suite updates
- ✓ Report confidence intervals
Evaluation Cost Optimization
Cost Comparison by Model Size
| Evaluation Suite | 7B Model | 13B Model | 70B Model | API (GPT-4) |
|---|---|---|---|---|
| Core Suite (MMLU + GSM8K + HumanEval) | $5 | $12 | $45 | $250 |
| Extended Suite (+10 benchmarks) | $25 | $60 | $220 | $1,200 |
| Full Comprehensive Suite | $80 | $190 | $750 | $4,000 |