Reasoning about why the magnitude of vectors generated by text-embedding-003-large is approximately 1
Analysis
This article explores why the vectors generated by OpenAI's text-embedding-003-large model tend to have a magnitude of approximately 1. The author questions why this occurs, given that these vectors are considered to represent positions in a semantic space. The article suggests that a fixed length of 1 might imply that meanings are constrained to a sphere within this space. The author emphasizes that the content is a personal understanding and may not be entirely accurate. The core question revolves around the potential implications of normalizing the vector length and whether it introduces biases or limitations in representing semantic information.
Key Takeaways
- •The article investigates the reason why text-embedding-003-large generates vectors with a magnitude close to 1.
- •It questions the implications of fixing the vector length to 1 in a semantic space.
- •The author acknowledges that the content is based on personal understanding and may not be entirely accurate.
“As a premise, vectors generated by text-embedding-003-large should be regarded as 'position vectors in a coordinate space representing meaning'.”