Human Evaluation
Definition
Human evaluation is the process of having people assess AI system outputs for quality dimensions that automated metrics cannot capture, including helpfulness, coherence, accuracy, style, and safety.
Why It Matters
Automated metrics lie. A model can score high on BLEU, ROUGE, and accuracy while producing outputs that humans find unhelpful, awkward, or wrong. Human evaluation catches what metrics miss: the subjective quality that determines whether users will actually trust and use your AI system.
For generation tasks especially, there’s no substitute for human judgment. Is this summary coherent? Is this response helpful? Is this code explanation clear? Is this chatbot response appropriate? These questions require human evaluators.
For AI engineers, human evaluation is essential but expensive. You can’t run it on every iteration like automated metrics. The skill is knowing when human evaluation is necessary, how to structure it for reliable results, and how to balance it with automated evaluation for efficiency.
Implementation Basics
When to Use Human Evaluation
- Final quality assessment before launch
- Comparing major model or prompt changes
- Evaluating subjective dimensions (helpfulness, tone, creativity)
- Safety and appropriateness testing
- Validating that automated metrics correlate with real quality
Evaluation Design
- Define criteria clearly: What exactly are evaluators rating? Helpfulness, accuracy, fluency, appropriateness?
- Create rubrics: Specific scoring guidelines reduce subjectivity. “A 5 means completely answers the question with no errors”
- Use multiple evaluators: Inter-annotator agreement reveals which judgments are reliable
- Include baselines: Compare outputs to existing systems, not just rate in isolation
- Randomize order: Prevent position bias when comparing multiple outputs
Evaluation Methods
- Absolute rating: Score each output on a scale (1-5 stars)
- Comparative ranking: Which output is better, A or B?
- Preference testing: A/B comparison with user choice
- Task completion: Did the output help users complete their goal?
Practical Considerations
- Internal team evaluation is fast but potentially biased
- Crowdsourcing (MTurk, Scale) provides diversity but requires quality control
- Domain expert evaluation is expensive but necessary for specialized content
- 50-200 examples often sufficient for statistically meaningful comparisons
Balance human evaluation cost with automated metrics. Use automation for iteration, humans for validation.
Source
Human evaluation remains essential for assessing generation quality, as automated metrics correlate imperfectly with human judgment on dimensions like fluency and usefulness.
https://arxiv.org/abs/2006.14799