Foundation and Large Language Models
Beyond improving the dataset, Cleanlab Studio allows you to train and deploy foundation models on messy real-world data with a few clicks. The AI that Cleanlab uses to detect issues in your dataset is powered by such models which are automatically fit your data.
Practice data curation like the best generative AI teams
Cleanlab software helps you effectively curate data without large teams of experts. Easily build your own data engine like those that power leading AI teams!
— OpenAI blog on DALLE-2, describing how they produce one of the best available generative image models.Since training data shapes the capabilities of any learned model, data filtering is a powerful tool for limiting undesirable model capabilities.
We prioritized filtering out all of the bad data over leaving in all of the good data. This is because we can always fine-tune our model with more data later to teach it new things, but it’s much harder to make the model forget something that it has already learned.
— Aidan Gomez (Founder & CEO of Cohere) speaking on the data sensitivity of LLM training on Weights and Biases podcast“If you teach the model something wrong, it will lock that in and forever believe that wrong thing.”
I was not prepared for how sensitive some of these models are.
— Emad Mostaque, founder and CEO of Stability.ai on Infinite Loops podcastData is the new oil but it's got to be clean data.
The valuable data here is the content that is structured in the world that allows you to learn principles as opposed to again, the big data age where it was about as much data as possible to extract patterns
— Nat Friedman, former CEO of GitHub, investor in hundreds of AI startups via StratecheryI do believe all the great labs are actually pouring huge amounts of energy into cleaning their data
— Andrej Karpathy, former Director of AI at Tesla, co-founder of OpenAI at Spark+AI SummitAt Tesla, I spend most of my time just massaging the datasets, and this takes a huge amount of work/effort and you want to do it extremely well.
Read more about this topic: Data Engine Design by George Pearse, ML Engineer at Binit.AI
Related applications
Data Annotation & Crowdsourcing
Data Entry, Management, and Curation
Customer Service
Content Moderation
CLEANLAB IS BUILT FROM THE GROUND UP TO SUPERCHARGE LLMS
- Cleanlab TLM (Trustworthy Language Model) that quantifies answer uncertainty
- Improve LLMs on Databricks with Cleanlab
- Improve LLM fine-tuning accuracy by 30% using Cleanlab
- Train LLMs in 1/3 the time and cost with Cleanlab’s ActiveLab
- Automatically detect errors in RLHF datasets like Anthropic's
- Improve evaluation data for better prompt engineering
- Ensure reliable few-shot prompt selection for LLMs
- Assess synthetic data produced via Generative AI
- Deploy more accurate ML models than fine-tuned OpenAI LLMs
for text classification of product reviews and legal judgements
Cleanlab featured in CB Insights GenAI 50 ranking as one of the world's 50 most innovative Generative AI companies (along with OpenAI, Hugging Face, Cohere, Anthropic, and more).
When fine-tuning OpenAI GPT models in a text classification task (politeness prediction), correcting label errors with Cleanlab Studio improved test accuracy by 37% without any change to the modeling/fine-tuning code (solely the dataset was modified). Read more.
Effortlessly detect errors in reinforcement from human feedback data (RLHF). Here is an example of a human error in the Anthropic RLHF dataset found with Cleanlab Studio, where the human-rejected LLM output (completion) is unequivocally better than the human-chosen LLM output (completion). The human who provided feedback just accidentally made a mistake! Read more.
Automatically flag low-quality examples for any image dataset. Cleanlab software can report which images are: blurry, under/over-exposed, oddly sized, low information, or (near) duplicates of others. Handling such issues is important in generative AI and computer vision (especially to diagnose spurious correlations). Read more.
Accelerate data labeling for Transformer Models. ActiveLab greatly reduces time and labeling costs to achieve a given model performance compared to standard data annotation. For example, ActiveLab hits 90% model accuracy at only 35% of the label spend as standard training. Read more.
