> ## Documentation Index
> Fetch the complete documentation index at: https://humandata.mangodesk.com/llms.txt
> Use this file to discover all available pages before exploring further.

# 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.

<Note>
  Evaluations are not just metrics—they're your compass for model improvement. Without proper evals, you're flying blind.
</Note>

## Structure of Evaluation Datasets

Each evaluation dataset contains three core components:

<CardGroup cols={3}>
  <Card title="Test Prompts" icon="message">
    Questions or tasks that test specific model capabilities
  </Card>

  <Card title="Ground Truth" icon="shield-check">
    Correct answers or validation criteria for scoring
  </Card>

  <Card title="Scoring Logic" icon="calculator">
    Mechanisms to evaluate model outputs against expectations
  </Card>
</CardGroup>

## Key Evaluation Challenges

### Benchmark Limitations

Standard benchmarks come with inherent challenges:

<Tabs>
  <Tab title="Common Benchmarks">
    **MMLU (Massive Multitask Language Understanding)**

    * 57 subjects across STEM, humanities, social sciences
    * Multiple-choice format
    * Risk: May not reflect real-world application needs

    **GSM8K (Grade School Math)**

    * 8,500 grade school math problems
    * Tests mathematical reasoning
    * Risk: Limited to basic arithmetic scenarios

    **HumanEval**

    * 164 Python programming problems
    * Tests code generation abilities
    * Risk: Doesn't cover all programming paradigms

    **Chatbot Arena**

    * Human preference comparisons
    * Real-world conversation quality
    * Risk: Subjective and resource-intensive
  </Tab>

  <Tab title="Contamination Risks">
    <Warning>
      Training data contamination occurs when evaluation examples leak into training datasets, inflating performance metrics without genuine capability improvement.
    </Warning>

    Prevention strategies:

    * Use recent, proprietary evaluations
    * Rotate evaluation sets regularly
    * Create hold-out sets from new data
    * Monitor for suspiciously high scores
  </Tab>

  <Tab title="Coverage Gaps">
    Standard benchmarks often miss:

    * Domain-specific requirements
    * Cultural and linguistic nuances
    * Real-world task complexity
    * Edge cases and failure modes
    * Multi-step reasoning chains
  </Tab>
</Tabs>

## Creating Custom Evaluations

<Steps>
  <Step title="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
  </Step>

  <Step title="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
  </Step>

  <Step title="Establish Scoring Criteria">
    Develop clear, reproducible scoring methods:

    * Automated metrics where possible
    * Human evaluation rubrics when needed
    * Combination approaches for nuanced tasks
  </Step>

  <Step title="Validate and Iterate">
    Ensure evaluation quality:

    * Test on known good/bad models
    * Verify inter-rater agreement
    * Adjust based on initial results
    * Document all assumptions
  </Step>
</Steps>

### Human Evaluation Approaches

<CardGroup cols={2}>
  <Card title="Likert Scale Rating" icon="star">
    Annotators rate outputs on scales (1-5, 1-7):

    * Helpfulness
    * Accuracy
    * Relevance
    * Clarity
  </Card>

  <Card title="Pairwise Comparison" icon="scale-balanced">
    Compare two model outputs:

    * Which is better?
    * By how much?
    * On what criteria?
  </Card>

  <Card title="Error Categorization" icon="tags">
    Classify types of failures:

    * Factual errors
    * Logic errors
    * Style issues
    * Safety violations
  </Card>

  <Card title="Task Completion" icon="clipboard-check">
    Binary success metrics:

    * Did it solve the problem?
    * Is the answer usable?
    * Does it meet requirements?
  </Card>
</CardGroup>