Question

Why does cosine similarity fail for short queries

Answer 1

Because very little text results in weak, noisy, and ambiguous vector representations, cosine similarity difficulties with brief queries.

Simply put, a three-word question lacks sufficient semantic signal for stable embedding geometry. 

For instance:

"reset password"

This might imply:

• API endpoint 

• database credential reset

• forgot password flow

• admin reset

• Resetting Active Directory

• SaaS account recovery with the Linux passwd command

Underspecification occurs in the embedding.

Fundamental Issue

The angle between vectors is measured via cosine similarity:

A × B = cos(θ)

When vectors include rich semantic information, this is effective.

Brief queries result in:

• low-information vectors

• unstable directed meaning 

• very wide semantic neighborhoods

A lot of unconnected pieces seem "similar enough."