Annotation Tools for SFT & RLHF Tasks
Build Robust SFT Dataset
Dialog Response Tool
Effortlessly construct SFT datasets with our intuitive Dialog Response Tool. Provide well-crafted example responses to prompts, guiding models to deliver answers that align with user expectations.
​Pretraining
SFT
RLHF
Refine Model Outputs with RLHF
Dialog Evaluation Tool
Tag and score pre-trained model responses based on custom criteria like relevance, helpfulness, and safety with our Dialog Evaluation Tool. Shape model behavior to generate human-preferred replies through Reinforcement Learning (RL).
Enrich Models with Contextual Data
Classification
Add situational metadata labels to dialog data, such as language, domain, and formality level. Enable large models to understand better and adapt to diverse conversation scenarios.
Dialog Visualization
Our annotation interface displays uploaded data as "User" and "Bot" chat bubbles.
Multimodal Support
Seamlessly visualize and annotate text + image Generative AI datasets.
Ontology Management
Build, reuse, and manage hierarchical Gen AI Ontology label assets.
Message Pinning
Pin up to 4 key messages on the interface for quick reference.
Scalable Annotation Workflow for Gen AI & LLMs
Scalable Pipeline
Effortlessly split tasks and efficiently manage roles & permissions for internal and external team members. Configure scalable annotation workflows on demand.
In-Depth Reports
Gain visibility from industry-leading performance tracking. Monitor project and task progress in real-time to keep everything under control.
FAQs
What is SFT? What is RLHF?
SFT (Supervised Fine-Tuning) is a supervised learning approach where models learn to map given inputs to outputs, maximizing next token prediction accuracy.
In RLHF, a reward model is trained to assess which responses best align with human preferences. This reward model then guides the language model to generate human-favored replies.
Both SFT and RLHF fine-tune models. SFT focuses on curating chat data, while RLHF also addresses safety, ethics, bias, and instruction following. RLHF improves a model's resilience, stability, and ability to provide detailed, user-aligned outputs.
What Gen AI data formats does BasicAI Cloud support?
BasicAI Cloud accepts user uploads of .json, .csv, .xlsx, .xls files and .zip, .gzip, .tar, .rar archives containing valid files, up to 100GB. Annotated data can be exported as BasicAI Generative AI formatted JSON.
We have a lot of data to be annotated. Do you have services for LLM data annotation?
Yes, BasicAI provides customized LLM & GenAI data annotation solutions, including data extraction, cleaning, labeling, RLHF, and model fine-tuning. We've helped AI leaders build intelligent chatbots and train proprietary small models. Learn more here.