Data labeling for 
Generative AI and LLM

Bring your Language Models to the next level with human input

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

Success story: Hugging Face and ServiceNow

Data is the key to success — whether you're 
building your own models or applying 
foundation models to your business


Toloka offers best in class expertise in fine 
tuning and evaluation of language models


Fine-tuning the model

Build safe and accurate language 
applications with high-quality custom data.

  • Easy access to multi-language data 
    with our global crowd
  • Domain-specific expertise with highly skilled 
    annotators (Mathematics, Programming, 
    Linguistics etc.)
Need fine tuning for your model?
Talk to our AI expert


  • Get instant feedback from 
    annotators to retrain the model
  • Rely on our experience with complex 
    labeling pipelines for exceptional 
    speed and accuracy
About RLHF at Toloka
  • Collect feedback via live human 
    interaction with the model
  • Use negative examples in your 
    model training 
Improve your language model with continual human feedback
Talk to our AI expert

Model evaluation

Continuous model evaluation is essential 
for consistent performance.

  • Monitor quality in production 
  • Obtain unbiased feedback 
    for model improvement

Quality metrics 
for your LLM

LLM output can be challenging to evaluate. 
NPS surveys and other user feedback risk introducing bias.
  • Rely on our industry experience with offline 
    evaluation to create custom quality metrics
  • Make metrics-based decisions before releasing 
    new model versions
Get better accuracy from your ML model
Talk to our AI expert

Your questions answered

  • Large Language Models (LLMs) are machine learning models that understand natural language and generate human language using deep neural networks. They are pre-trained on massive text datasets, such as books, articles, and web pages, which means they perform well with general-purpose output. But they can also be fine-tuned to handle specific tasks and domains that involve language understanding, like translation, question answering, writing stories, or chatbot development.
  • Models like GPT-3 are popular for natural language processing tasks. However, many businesses lack the resources and expertise to work with them. Toloka automates model fine-tuning, evaluation, and monitoring — so you can get your AI application up and running without hiring a team of experts.
  • Large language models (LLM) that have been pre-trained with English data can be fine-tuned with data in a new language. The amount of language data required for fine-tuning is far less than the huge training dataset used for the initial training process of a large language model.Our huge global crowd can generate high-quality training data in every major world language.
  • The best way to ensure that your language model is safe for users is to use human evaluation to detect any potential bias in the output. You can also use a combination of natural language processing (NLP) techniques and human moderation to detect any offensive content in the output of large language models.Toloka can help you set up an efficient moderation pipeline to make sure that your large language model output conforms to your corporate policies.
  • Our global crowd spans 100+ countries with 40+ languagesOur skilled annotators have diverse backgrounds with expertise in a wide range of fieldsSelect annotators for your project by country, language, skill, and expertiseLearn more about the Toloka crowd
  • Large language models require a large amount of data to train, and the data needs to be labeled accurately for the language model to make accurate predictions. Humans can provide more accurate and nuanced labeling than machines. Without enough diverse data, language models can become biased or inaccurate. Human labeling can help guarantee that the data is balanced and representative of real-world use cases. Large language models are also prone to hallucinations, or inventing output that isn't based on facts. Human evaluation of model output is essential for aligning the model with expectations.
  • Pricing of particular human tasks for LLM development depends on many factors, including the purpose of the model. Please contact our LLM experts to get a quote.
  • Your data that is used in any tasks related to LLM development is private and belongs to you. It will not be reused for training other models, or for any other purposes.
  • It is assumed that the model hosting is on the client side and Toloka provides human input for its development.
  • Large language models work well for generalized tasks because they are pre-trained on huge amounts of unlabeled text data, like textbooks, dumps of social media posts, or massive datasets of legal documents. But to get good at a specific task, language models need fine-tuning and human feedback. If you are developing your own LLM, you need high-quality labeled data.Toloka provides human-labeled data for your language model development process. We offer custom solutions for:
    • Dataset collection and cleaning for the initial training stage
    • Labeling training data to fine-tune the language model
    • Model tuning (creating prompts and instructions; moderating, categorizing, validating, or responding to prompts)
    • Reinforcement learning from human feedback (RLHF) workflows
    • Evaluating quality of model output
    • Moderating model output

Accelerate time-to-value for your LLM