Elevate the Value of
Your Data Annotation
Try Toloka Hybrid Labeling for high-quality, fast data annotation in over 40 languages—boosting your team's efficiency and ROI through a blend of LLM+human expertise.Try Toloka Hybrid Labeling for high-quality,
fast data annotation in over 40 languages—boosting
your team's efficiency and ROI through a blend of
LLM+human expertise.Try Toloka Hybrid Labeling for high-quality, fast data annotation in over 40 languages—boosting
your team's efficiency and ROI through a blend of LLM+human expertise.
The new frontier of enterprise-ready data annotation
Fine-tuned LLMs
Human signals
Included in Gartner's 2023 Hype Cycle for Generative AI report
Can LLMs be trusted for your annotation?
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Outdated pre-training dataset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
Yes, with human supervision
- Get complex tasks decomposed to simple ones by our team to have them labeled by the most suited level of the pyramid.
- LLM only, human only, or a mix of both to get up to 130,000 labels/day of the highest quality.
Data Labeling for NLP
Get high-quality training data developed specifically to match the needs of your Natural Language Processing (NLP) engine. With Toloka Hybrid Labeling you can get annotations for Named Entity Recognition (NER), Sentiment Analysis, Speech Recognition, Text and Intent Classification, Audio transcription, Text recognition, and much more.
Data Labeling for Computer Vision
Label images and videos and maximize the potential and efficiency of training data. Toloka Hybrid Labeling supports annotation for image classification, semantic segmentation, object detection and recognition, and instance segmentation. Labeling tools include bounding boxes, polygons and keypoint annotation.
Custom-made for your needs
GDPR, ISO 27001 and ISO 27701 compliant
API
24/7 global support
Managed service
FAQ
- Accuracy measurement is always defined with the client and based on the individual use case.
With that being said, the majority of tasks are assessed using Percentage Agreement techniques with overlap. That means that there needs to be a statistically significant consensus on the correct answer. If this is not reached immediately, more labelers are brought in to reach the SLA threshold. Keep in mind that this approach is rendered much more efficient and cost-effective by Toloka’s proprietary task decomposition framework, which ensures quality is as high as possible without creating unneeded expense. - Having a Company whose only focus for 10+ years has been on enterprise-ready data labeling ensures you get the best possible expertise and efficiency while your team learns best practices. Scale up your operations keeping the quality as high as your team needs without needing to learn by making expensive mistakes.
- Labeling with large language models grants you lower quality than a hybrid approach, and increases the chance of introducing unrealistic information paired with biases and ethical problems.
These are mitigated when pairing LLM-generated labels with human signals from a crowd from over 100 countries, ensuring Toloka works for you and your data 24/7 across all data types (including Text, Visual, Web-based, or their combination) and 40+ languages. - In most cases yes. Toloka Hybrid Labeling can be hybrid also when it comes to the origin of the labels. As a company, we offer a variety of enterprise-tested LLMs fine-tuned specifically towards data labeling and a seamless pipeline for them, but we understand that you might have some specific needs or requirements for which you might not want to or be able to use them.