Learn how Anthropic partnered with Surge AI to gather high-quality human feedback at scale using the RLHF platform, resulting in one of the safest and most advanced large language models on the planet.

We analyzed HellaSwag, a popular LLM benchmark, and found errors in 36% of its rows.

Last year, Google released their “GoEmotions” dataset: a human-labeled dataset of 58K Reddit comments categorized according to 27 emotions. The problem? A whopping 30% of the dataset is mislabeled! Check out some of the egregious errors, and learn how to build better datasets.30% of Google's Emotions Dataset is Mislabeled

We built a dataset of 8,500 Grade School Math Problems for OpenAI. The goal of the dataset: to train language models like GPT-3 to solve natural language math problems and measure their reasoning ability. Learn about our process in this blog post!

Where do human artists fit in a world of rich, creative AI? We asked 100 Surgers to draw the DALL-E prompts.

Data-centric AI requires radically rethinking the data that goes into your models. Surge AI provides data labelers with the skills you need to get context-sensitive labels.

In theory, AI has blown past our wildest dreams; in practice, Siri can’t even tell us the weather. The problem? Creating high-quality datasets to train and measure our models is still incredibly difficult. We should be able to gather 20,000 labels for training a Reddit classifier in a single