Training and evaluation data for foundation models
What Tasq.ai does for foundation model teams
Foundation model developers need to know how their models perform on outputs that are complex, subjective or specialized. Tasq.ai provides evaluation and validation for those outputs, combining model, crowd and expert judgment. We also annotate the data models learn from, across text, video, audio and images.
Why foundation model outputs are hard to evaluate
Two model versions can produce outputs that are close in quality. The differences may be subtle, subjective, or depend on the quality dimension being assessed. Some judgments can be automated. Others depend on human perception, language or cultural context. Specialized and ambiguous questions need domain experts. And sometimes the evaluation rubric does not exist yet and has to be developed first.
How Tasq.ai evaluates model outputs
Tasq.ai breaks the evaluation into smaller decisions and routes each one to the right judgment: Model as a Judge when a model can decide with confidence, Crowd as a Judge when it takes human perception or preference, and Expert as a Judge when the question is specialized or ambiguous. Layers can run in parallel, and confidence assessment shows where further judgment is needed.
Comparing model versions
Two model versions are evaluated on the same prompts across defined quality dimensions. You get two levels of evidence: an item-level judgment on each pair of outputs, with a confidence assessment, and model-level evidence accumulated across pairs for each quality dimension. This can establish consistent differences even when single-reviewer choices show no clear winner.
In one documented comparison for a frontier generative-video lab, two image-editing models were assessed on four quality axes. A single in-house reviewer resolved a winner on one of the four. Crowd as a Judge reached a statistically significant winner on all four.
What you get
- Evidence to support your release decision, including relative performance and the uncertainty around it. The decision stays yours.
- Access to a global network of 100M+ contributors and 75,000+ verified domain experts, in 120 languages.
- Evaluation rubrics developed where none exist yet.
- Judgments and corrections that feed back into the system and improve subsequent work.