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!