Large language models (LLMs)
Leading ML teams are quickly capitalizing on the latest advances in large language models (LLMs) as a powerful starting point for their NLP use cases. However, they often realize that these base models need to be specifically tailored for their use case and have to be retrained with contextual annotated data in order to build production-grade AI.
Learn how ML teams in industries such as healthcare, retail, and beyond can enable breakthroughs faster with Labelbox, whether it’s summarizing medical research papers, categorizing customer sentiment, or delivering chatbots with human-like interactions.
Powering intelligent applications from leading enterprises
Labeling apps for reinforcement learning with human feedback (RLHF)
Use off-the-shelf labeling apps or build custom ones with the Labelbox SDK for reinforcement learning with human feedback (RLHF) use cases. Advance state-of-the-art language models that align with human preferences so that your applications can best understand what your customers and users want.
Fine-tune your pre-trained LLM models to output human-like responses faster with a complete set of labeling editors that can be tailored for summarization, code completion, sentiment detection, and beyond.
Language is nuanced. Your AI models should be too.
Labelbox provides a seamless annotation experience that allows you to label, review, and manage your custom data in its native format: text, conversation, PDFs, etc. Generate ground truth to refine your large language models using our powerful text editor that supports classifications, entity recognition, relationships on raw text snippets or threaded conversations.
Track down the most relevant data at lightning speed
Use Labelbox Catalog to visualize all of your labeled and unlabeled text data using filters for metadata, model inferences, and other attributes like embedding similarity. Find the most relevant data to label from your large-scale datasets and send the data directly to your labeling project in just a few clicks.
Label faster than ever with automation
Achieve up to 65% in labeling efficiency gains with model-assisted labeling – use your large language model to pre-label data, and let your team of labelers focus on corrective actions to generate ground truth so they don’t need to start from scratch.
Save time by uploading your foundational model predictions directly into your annotation experience. Simply pick any foundational model (GPT, BERT, etc) including ones that have already been re-trained which are closest to your use case (KeyBERT, finBERT, LEGAL-BERT).
Jumpstart your large language models with the best labeling teams and linguists
Access the world’s best data labeling teams to label your data on demand, at scale. We offer support in numerous domains, including content moderation in over 20 languages.
