Supervised Fine-tuning for LLMs
We offer end-to-end data labeling services for supervised fine-tuning of large language models to expand task-specific model capabilities, improve accuracy and mitigate biases. Our experts deliver high-quality single-turn and multi-turn conversation data across a diverse range of domains and languages.
General Q&A Data Generation
Mathematics Q&A Data Generation
Expand model capabilities with curated, high quality datasets for SFT
Acquiring large volumes of high-quality labeled data requires access to vetted domain experts, while ensuring consistent annotations demands rigorous workflow management and quality control. Leverage our universal labeling platform backed by a global network of subject-matter experts (PhDs, engineers, industry specialists) to generate precise, tailored datasets across a diverse range of domains.
Mathematics
Coding
Language
Science
Medicine
Law
Finance
History
And more
Recent Project Experience
| Description | Domain | Modality | # of Experts | Duration | Accuracy |
|---|---|---|---|---|---|
Search results summarization for fine-tuning query performance | Language | Text | 35 | 13 months | 90% (80% req.) |
Video-to-text dataset creation for enhancing visual-language understanding | Multi-modal | V2T | 15 | 2 months | 90% (90% req.) |
Competition-level math problem-solution dataset creation for fine-tuning of reasoning capabilities | Math | Text (Lean) | 5 | 2 months | 85% (85% req.) |
Academic & real-world finance QA dataset creation for fine-tuning of finance reasoning capabilities | Finance | Text | 10 | 3 months | 87% (85% req.) |
NLP dataset creation for improving model safety | Language | Text | 9 | 6 months | 85% (80% req.) |
Search results summarization for fine-tuning query performance
Domain
Language
Modality
Text
# of Experts
35
Duration
13 months
Accuracy
90% (80% req.)
Video-to-text dataset creation for enhancing visual-language understanding
Domain
Multi-modal
Modality
V2T
# of Experts
15
Duration
2 months
Accuracy
90% (90% req.)
Competition-level math problem-solution dataset creation for fine-tuning of reasoning capabilities
Domain
Math
Modality
Text (Lean)
# of Experts
5
Duration
2 months
Accuracy
85% (85% req.)
Academic & real-world finance QA dataset creation for fine-tuning of finance reasoning capabilities
Domain
Finance
Modality
Text
# of Experts
10
Duration
3 months
Accuracy
87% (85% req.)
NLP dataset creation for improving model safety
Domain
Language
Modality
Text
# of Experts
9
Duration
6 months
Accuracy
85% (80% req.)
Our Global Expert Workforce
