Isaacus 工程师做了一款 Wordle 风格猜词游戏来教嵌入向量

Hey r/MachineLearning I'm Abdur-Rahman Butler, a machine learning engineer at Isaacus.

I made an interactive write-up that walks you through the A to Z of vector embeddings, inspired by Brendan Bycroft's incredible interactive LLM visualisation guide (see here - https://bbycroft.net/llm).

Most of my work is on encoder models, and I kept noticing how few good interactive resources there are on embeddings. The ones that do exist tend to either plot some embeddings in 3D or only explain a narrow applied technique like semantic search or RAG.

So I wanted to make something that teaches how embeddings work by letting you play with them. In the write-up, there are interactive visualisations that allow you to explore the concepts of vectorisation, cosine similarity, semantic search, hierarchical retrieval, clustering, zero-shot classification and deduplication.

The centrepiece of the post is a game I've dubbed 'SemanticGuessr' (see above). It's inspired by Wordle and GeoGuessr, and the goal is to draw a loop around a random target word by triangulating guesses on a 2D map of 43,339 common English words.

It's admittedly a bit intimidating at first, as semantic space does not perfectly match our intuition of meaning. But once you get the hang of it, the game becomes really addictive. I recently had the pleasure of demoing the game to some law students at the University of Melbourne, and we had a lot of fun throwing different vector equations at it to see where they'd land.

Some fun ones to get started with:

 london - english + french running - land + water avg(breakfast, lunch, dinner) 0.4 * noodles + 0.6 * pasta 

I've also included Python snippets alongside some of the visualisations if you want to follow along with the techniques later. The game runs statically in your webpage. I managed to achieve this by reducing the embedding dimensions down to 512 and quantising each value to 4 bits. This brought the total payload down from 311 MB to 12.3 MB with only a minimal impact on precision (around 6%).

It was great fun to make, and I hope you have fun playing around with it. Happy to take any questions, and if there's enough interest I might set up a dedicated games page for some of the other encoder-based games I've been working on.

Do let me know what highest score you get based on your target word and chosen path :)

LINK TO PLAY: https://isaacus.com/blog/what-are-vector-embeddings