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Our comprehensive evaluations use expert knowledge to align model performance with your expectations, ensuring
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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
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choose:
Domain specific datasets
Deep knowledge domains
Industry domains
Skill-Scenario based datasets
Specific skills performance
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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
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When to
choose:
Human-driven evaluation
One-time evaluation
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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
Learn more about Toloka evaluation for GenAI
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Why is AI evaluation important?
Expert evaluation is essential for every AI use case. Testing applications powered by advanced technologies, including LLMs, is crucial to ensure stability, reliability, and cost-effectiveness under various conditions. Expert evaluation also prioritizes response quality, using both qualitative and quantitative metrics to assess how closely generated responses match reference answers.
What are the key LLM evaluation metrics?
LLM evaluation metrics are fundamental for understanding how well models perform across a variety of tasks. These metrics fall into two main categories: intrinsic and extrinsic. Intrinsic metrics, such as perplexity, focus on the model’s internal consistency and ability to predict text, while extrinsic metrics—like accuracy, F1-score, semantic similarity, and exact match—measure how the model performs on real-world tasks and benchmarks. Factual correctness is another critical metric, especially for applications where reliable information is paramount.
To gain a holistic view of a model’s capabilities, it’s important to use a combination of these evaluation metrics. For example, semantic similarity can help assess how closely an LLM output matches a reference answer, while exact match is useful for tasks with a single correct response. Performance and load testing are also essential, as they reveal how the model handles increased user demand and complex interactions. Rigorous testing practices—including functional testing, regression testing, and security testing—ensure that large language models remain robust, reliable, and secure as they evolve. By leveraging a diverse set of LLM evaluation metrics, organizations can confidently measure and improve their LLM systems.
How to test an LLM model?
Functional testing
Functional testing is a cornerstone of ensuring that LLM models deliver on their core functionality. This process involves systematically evaluating the model’s ability to generate coherent, contextually appropriate, and human-like responses to a wide range of prompts. Techniques such as unit testing allow developers to isolate and verify specific components or behaviors of the model, while integration testing examines how these components interact within the broader system. Regression tests are crucial for confirming that updates or modifications to the model do not inadvertently introduce new issues or degrade performance.
When testing LLM models, it’s important to assess their performance across various tasks, including text generation, summarization, and conversational dialogue. Special attention should be paid to how the model handles edge cases and adversarial inputs, as these scenarios often reveal vulnerabilities or limitations in the model’s reasoning. By adopting a comprehensive approach to functional testing—including unit tests, regression testing, and scenario-based evaluations—organizations can ensure that their LLM models consistently meet user expectations and maintain high standards of quality.
CI/CD for Model Development Implementing
CI/CD (Continuous Integration/Continuous Deployment) pipelines is essential for the efficient and reliable development of large language models. CI/CD automates the process of testing and deploying new model versions, ensuring that every change is thoroughly validated before reaching production. By integrating unit tests, regression tests, and other testing methods into the CI/CD workflow, teams can quickly identify and address issues, reducing the risk of introducing errors or performance regressions.
Automated tools play a key role in this process, enabling rapid evaluation of model performance and providing actionable feedback to developers. This not only accelerates the development cycle but also enhances the overall quality and reliability of the LLM system. With a robust CI/CD pipeline in place, organizations can confidently iterate on their models, deploy updates seamlessly, and maintain a high level of trust in their AI-powered applications.
Why is data important for testing model reliability?
High-quality data is just as essential for evaluating large language models (LLMs) as it is for training them. Effective evaluation depends on diverse, representative, and well-curated datasets that reflect real-world use cases and potential edge scenarios. Without solid evaluation data, it's impossible to accurately measure a model’s performance, fairness, or reliability.
Robust evaluation datasets help uncover how well a model generalizes, how it handles ambiguity or bias, and how it performs across different domains or user groups. They also support regression testing and continuous monitoring, enabling teams to detect degradation or unintended behavior over time.
In short, reliable evaluation starts with reliable data. Investing in thoughtful data design for testing and validation is a critical step in building trustworthy and high-performing LLMs.
What are the main challenges in performance evaluations?
Testing large language models (LLMs) is uniquely challenging due to their scale, complexity, and the unpredictable nature of their outputs. A key difficulty lies in evaluating performance across diverse tasks and datasets, which demands a multifaceted approach. Functional, regression, and security testing are all essential to ensure robustness and reliability.
Assessing how models handle edge cases and adversarial inputs is equally important, as these scenarios often reveal weaknesses in reasoning or factual accuracy. Stress testing and performance degradation analysis help uncover how models behave under heavy load or in unexpected conditions—insights that are crucial for production readiness.
Moreover, the evaluation process can be resource-intensive, requiring both computational power and specialized expertise. To keep pace, organizations are increasingly adopting automated testing methods and integrating CI/CD pipelines. These tools streamline evaluations, support continuous monitoring, and help teams maintain high standards of quality in even the most demanding AI environments.