Take note, OpenAI constructs prompts using markdown. I believe this is reflective of their instruction tuning datasets. In our own work we’ve seen markdown increase model compliance far more than is otherwise reasonable.

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"Open AI is not going to make it" Jeremy Howard called it yesterday 🤯 Really prescient. h/t Vanishing Gradients Podcast podcast

According to the story, Zoroastrians were the wise men (Three Magi) who traveled from Persia to visit Jesus, Mary, and family, bringing gifts to celebrate an amazing new soul, the “king of the Jews.” Only 100k of us Zoroastrians left in the world. Doing my part with 2 kids! 😃

Did anybody try using genetic programming to improve LLM Agent's prompts? You let a bunch of them run with somewhat different prompts/rules/guidelines. Then combine the best pairs to form the next generation. You could also just make mutations (asexual reproduction), that gives

this was a neat little read. the idea is to do data augmentation with embeddings. they randomly sample around an embedding and then decode with vec2text. there is a trick to randomly sampling while not leaving the embedding manifold; they try to sample within an embedding "cone"

enjoyed reading this. useful insights into structures in latent space.

Evaluating a model on training data is like asking your mom if you look good.

If you want to demonstrate that it is impossible to do X (here, X="spot the bot"), you shouldn't ask random people to do X, you need to ask people who actually know how to do X (experts) and see how the *best* of them perform. Otherwise you get results like "22% of our judges

A new paper in Nature found that you cannot, in fact, train AIs on AI-generated data and expect them to continue improving. What happens is actually that the model collapses and ends up producing nonsense.

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