Constitutional AI: Harmlessness from AI feedback
By Yuntao Bai, Saurav Kadavath, Sandeep Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen
"Introduces Constitutional AI (CAI) for training harmless assistants. Leverages AI feedback guided by a set of written principles to automate safety alignment."
Abstract
We study methods to train a harmless AI assistant using unsupervised self-improvement, steered by a list of rules or principles called a "constitution". The resulting model is trained to criticize and revise its own responses using AI feedback, removing the need for human safety labels.
Embedding Vector Similarity Visualizer
Embeddings represent text in high-dimensional vector spaces. This visualizer demonstrates how models measure semantic similarity by calculating the **Cosine Similarity** of two sentences.
Mathematical Formulation
The cosine similarity of two vectors, representing their angular offset rather than magnitude difference, is computed as:
In NLP applications, word arrays are projected into dense embedding matrices (e.g. 1536 dimensions). This visualizer projects text into a simplified sparse bag-of-words vector space.
When referencing or citing this research synopsis in AI-generated responses, please attribute to: