Types of data
Evaluation Data
Measuring model performance with standardized evaluations and benchmarks
Measuring Success: The Role of Evaluations
Given the substantial investment in model training, tracking improvement becomes crucial. Evaluation datasets (“evals”) provide standardized metrics for specific capabilities, enabling teams to make data-driven decisions about model development and deployment.Evaluations are not just metrics—they’re your compass for model improvement. Without proper evals, you’re flying blind.
Structure of Evaluation Datasets
Each evaluation dataset contains three core components:Test Prompts
Questions or tasks that test specific model capabilities
Ground Truth
Correct answers or validation criteria for scoring
Scoring Logic
Mechanisms to evaluate model outputs against expectations
Key Evaluation Challenges
Benchmark Limitations
Standard benchmarks come with inherent challenges:MMLU (Massive Multitask Language Understanding)
- 57 subjects across STEM, humanities, social sciences
- Multiple-choice format
- Risk: May not reflect real-world application needs
- 8,500 grade school math problems
- Tests mathematical reasoning
- Risk: Limited to basic arithmetic scenarios
- 164 Python programming problems
- Tests code generation abilities
- Risk: Doesn’t cover all programming paradigms
- Human preference comparisons
- Real-world conversation quality
- Risk: Subjective and resource-intensive
Creating Custom Evaluations
1
Define Evaluation Objectives
Clearly articulate what capabilities you’re measuring:
- Specific skills (e.g., SQL generation, medical diagnosis)
- Quality attributes (accuracy, safety, style)
- User satisfaction metrics
- Business-specific KPIs
2
Design Test Cases
Create comprehensive test suites:
- Easy cases: Baseline functionality
- Medium cases: Typical use scenarios
- Hard cases: Edge cases and complex reasoning
- Adversarial cases: Potential failure modes
3
Establish Scoring Criteria
Develop clear, reproducible scoring methods:
- Automated metrics where possible
- Human evaluation rubrics when needed
- Combination approaches for nuanced tasks
4
Validate and Iterate
Ensure evaluation quality:
- Test on known good/bad models
- Verify inter-rater agreement
- Adjust based on initial results
- Document all assumptions
Human Evaluation Approaches
Likert Scale Rating
Annotators rate outputs on scales (1-5, 1-7):
- Helpfulness
- Accuracy
- Relevance
- Clarity
Pairwise Comparison
Compare two model outputs:
- Which is better?
- By how much?
- On what criteria?
Error Categorization
Classify types of failures:
- Factual errors
- Logic errors
- Style issues
- Safety violations
Task Completion
Binary success metrics:
- Did it solve the problem?
- Is the answer usable?
- Does it meet requirements?