Deep Evaluation
for GenAI

LLM evaluation platform with quality metrics
to fit every model and scenario.

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Evaluation

We know how to measure the quality of LLMs

Traditional metrics can't tell you whether your LLM is accomplishing your goals. The Toloka Deep Evaluation platform bridges the gap between business problems and technology — with the most extensive range of quality metrics, and evaluation pipelines customized to your business context.
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Be confident in your LLM's performance with our reliable evaluation framework

  • Don't let your model give customers false information, or harm revenue with poor fact-checking
  • Evaluate your model in real-world scenarios and make enhancements to align with your goals and protect your business from risks
  • Make sure you're adopting the best base LLM for your application
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Deep evaluation empowers your team to align model 
performance with your expectations and ensure model 
output is accurate, reliable, and responsible.

Why Toloka Evaluation

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    Tailored performance metrics
    Custom evaluation plan designed for your unique business case with the most extensive range of quality metrics in the market
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    Scalable human insight
    Reliable results from our skilled annotators and domain experts in fields like computer science, EU law, medicine, and more
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    In-depth evaluation
    Our evaluation pipelines harness LLMs, automated algorithms, and human expertise for deep insights and detailed reports
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We capture the right metrics for your GenAI application

Truthfulness
  • General factuality
  • Context attribution
Skills
  • Instruction following
  • Reasoning
  • Context understanding
  • Domain knowledge
  • Logical consistency
  • Actions recognition
Creativity
  • Originality
  • Diversity
Helpfulness
  • Relevance
  • Conciseness
  • Completeness
Style
  • Lexical complexity
  • Tone
  • Moralizing
  • Engagement
Language
  • Grammar
  • Comprehensibility
  • Coherence
  • Repetition
Structure
  • General formatting
  • Tech formatting
  • Citations
Harmfulness
  • Fairness and bias
  • Insult, hate, offensive
  • Threat, violence
  • Spam, promotions
  • Sexual
  • Drugs, alcohol
Safety
  • Memorization of copyrighted/licensed material
  • Private identifiable information
  • Robustness

Deep Evaluation in practice

We analyze each model's unique usage scenario and capture the best metrics to measure model performance
ImageClient:Perplexity AITask:Evaluating helpfulness of gpt-3.5 (RAG) vs. pplx-70b-onlineGoal:Select the optimal model for a conversational search 
interface and get deep insights into model qualityMetrics evaluated:Helpfulness, Truthfulness, SafetyLink to the full case
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Ready for your own evaluation?
Talk to us
Ready for your own evaluation?
Talk to us

How we do evaluation

  • 1

    Analyze your model's performance and usage scenario.
  • 2

    Propose evaluation metrics that fit the unique context and goals of your model.
  • 3

    Create an evaluation pipeline that combines automation and human labeling.
  • 4

    Deliver comprehensive reports with insights on how to improve your model.
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Try Toloka Deep Evaluation 
for your project

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Customers who trust us