Automated data labeling
with LLMs and humans
Build the ideal data pipeline with LLMs, our trained crowd, and experts
for optimal price, quality, and throughput
Who is labeling your data?
LLMs now offer a powerful data labeling option, but the decision to use LLMs isn't straightforward.
Tap into Toloka's AI expertise to design a data labeling strategy for your next project
Talk to our AI experts
Quality. LLMs perform on par or better than humans in some tasks, but need guidance.
Speed. LLMs can offer better throughput, but may need fine-tuning.
Price. LLMs might be cheaper or more expensive than manual labeling, depending on the project setup.
We leverage machine learning and human
expertise for optimal speed and quality
Toloka has the know-how to apply LLMs, domain experts, and data quality controls exactly where you need them.
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How Toloka optimizes data labeling pipelines with LLMs
LLM-boosted
human data labelingLLM offers suggestions
for human annotators
LLM data labeling
with human assistanceHumans label edge cases
not handled by LLM
Human evaluation
of LLM annotationHumans perform selective evaluation
of LLM annotations
LLMs need human oversight
Here are just a few examples of data labeling challenges for LLMs
Ambiguous tasks
- Task type:Search relevance
- Issue:Ambiguity in "reasonably well" type of instructions
- Reason:LLMs often have different logic from humans
Outdated pre-training dataset
- Task type:Product similarity
- Issue:New products or new brands
- Reason:LLMs hallucinate about things they
were not trained on
LLMs incorrectly interpret
slang and shorthand
- Task type:Data cleaning
- Issue:Typo correction
- Reason:LLMs aren't trained to understand
all languages equally well
We find the best language model for your task
and support it with human input for reliable results
Guidance for LLM implementation in annotation projects | Open AI type LLMs | Smaller LM fine-tuned on project data |
| Available data | Few shots | 10K labelled examples |
| Type of tasks | Tasks within domain of tasks best performed by LLMs | Wide range of tasks |
| Flexibility | Flexibility within available context window Inability or high cost of fine-tuning | Relatively low cost of fine-tuning |
| Cost | High cost per label | Low marginal cost per label after fine-tuning |
Toloka's bespoke solutions combine the speed of LLMs and
the insight of our crowd for optimal data labeling performance
We take care of prompt engineering and quality control to deliver the best results for your project requirements.
Success story: Hugging Face and ServiceNow
Why Toloka
Our solutions combine state-of-the-art ML and crowdsourcing technologies,
supported by a global crowd of annotators and secure infrastructure.
Powering new language models and research
Our experience working with natural language processing solves real-life business problems and helps advance
scientific research and open-source projects with large language models.
Get best-in-class data labeling solutions with LLMs