📊From Performance Review to Quality Assessment: Our New Evaluation Approach
As our GenAI G11n Assessment Model continues to evolve, we have been reviewing not only the evaluation results, but also the methodology behind them.
One of the most important changes has been the transition from a Performance Review approach to a broader Quality Assessment approach.
🔵 BEFORE — Performance Review
Our previous evaluation focused primarily on comparing model performance based on a single execution per prompt.
The main question was:
Which model achieved the highest score?
While this approach was useful for an initial performance comparison, a single execution provides only a point-in-time view of a generative AI model.
It can miss:
- Intermittent failures
- Response variability
- Inconsistencies across executions
- Differences in model behavior
Previous approach: performance benchmarking based on a single execution.
🟢 NOW — Quality Assessment
Our evaluation framework has evolved to assess not only whether a model produces a correct response, but also how it behaves across repeated executions and different localization scenarios.
The same prompt can produce different outputs when executed multiple times. Repeat sampling allows us to capture this variability and identify behaviors that may not be visible in a single execution.
Our current approach focuses on:
- Quality
- Consistency
- Reliability
- Localization accuracy
- Instruction following
- Model behavior across executions
Current approach: quality assessment through repeated executions and aggregated results.
🔄 Why Repeat Sampling?
Generative AI models can produce different responses to the same prompt.
By executing prompts multiple times, we can distinguish between different types of model behavior:
| Behavior | What We Can Observe |
|---|---|
| Consistent | The model repeatedly produces the expected type of result. |
| Intermittent | An issue appears only in some executions. |
| Variable | The model produces meaningfully different outcomes across executions. |
This provides a more representative view of model behavior than evaluating a single response.
📈 From a Single Score to a Quality Profile
This evolution also changes how we interpret our results.
Instead of asking only:
"Which model scored higher?"
we can now explore:
- How consistently does the model perform?
- What types of issues occur?
- How frequently do they occur?
- Are failures consistent or intermittent?
- Which quality dimensions are affected?
- How reliable is the model across localization scenarios?
This allows the evaluation to provide a quality profile rather than simply a model ranking.
🎯 Our Goal
The objective is to build an evaluation methodology that provides a more representative view of generative AI quality by combining:
Quality + Consistency + Reliability + Localization
The goal is not simply to identify the model with the highest score, but to better understand model behavior and its potential impact in real-world localization scenarios.
🚀 What's Next?
As the GenAI G11n Assessment Model continues to evolve, we will continue refining our evaluation methodology and expanding our ability to assess:
- Model consistency
- Stability and reliability
- Failure patterns
- Localization quality
- Multimodal scenarios
- Quality across future releases
We are moving from measuring model performance to assessing model quality, consistency, and reliability.
This transition gives us deeper insights into model behavior and helps us build a more meaningful evaluation framework for generative AI in localization.