Expert data for LLM testing
Our comprehensive evaluations use expert knowledge
to align model performance with your expectations, ensuring
AI outputs are accurate, reliable, and socially responsible.
Trusted by Leading AI Teams
Test your model across any domain or language
Our vetted domain experts have Masters or PhD degrees, and/or extensive work experience in their industry.
90+ Domains
Sciences & Industries
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20+ Languages
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Expert evaluation for every AI use case
Toloka Evaluation: powering frontier models
Toloka specializes in combining human expert knowledge with technology to evaluate frontier-pushing LLMs and AI Agents.
Our focus on real-world evaluation tasks and environments helps you better understand the actual capabilities and limitations of your models.
Custom datasets
Improving code understanding and explanation capabilities for foundational coding model
Domain specific and Skill‑Scenario based datasets
Evaluation
types:
Deep knowledge domains
Skills
Scenarios
Industry domains
When to
choose:
Domain specific datasets
Deep knowledge domains
Industry domains
Skill-Scenario based datasets
Specific skills performance
Multi-modal tasks
Interaction scenarios
Evaluations aligned with your goals
We target evaluations to your model's capabilities using expert human raters or automated scoring.
Human‑driven and automated evaluation
Evaluation
types:
Pointwise evaluation
Interactive evaluation
Golden answer evaluation
Rubric-based evaluation
Side-by-side evaluation
Red-teaming
When to
choose:
Human-driven evaluation
One-time evaluation
Qualitative insights by experts
Creative modality
Automated evaluation
Interactive evaluations
Text output
Case studies
Domain Specific
Evaluation Dataset
Client type:
Leading AI Company
Data type:
Rubric-Based Evaluation Dataset
Experts:
MA & PhD in Linguistics
Language:
English
Volume:
400 datapoints
Application:
Evaluating frontier model for complex domain knowledge & reasoning
Long-Context Red-Teaming
Client type:
Big tech
Data type:
Human evaluation
Experts:
Skilled Editors
Language:
English
Volume:
500 datapoints
Application:
Identifying model vulnerabilities
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