Why do single vector representations fail?

This week we’ll talk a bit about late interaction. But to get there, we need to think about why single vector representations fail.

Let’s think about restaurants.

Here’s an article reviewing local restaurants. I have three Italian restaurants and two Chinese ones.

What’s the average of these? Russian or something!? Maybe Middle Eastern food?

If my document lists these restaurants, then that’s exactly what I’ll get in a single vector encoding. A confusing muddle somewhere in the middle of every cuisine.

The user comes along looking for “best Italian restaurant in my town” and they don’t get my document listing 3 Italian and 2 Chinese. Because the cosine similarity between the query, “italian restaurant,” and this document, lost somewhere in the Middle East, has become so low.

As documents grow in complexity, the problem only worsens

This sort of failure mode happens all the time with embeddings, where forever reason the whole washes out the parts.

In any information-heavy search, the tension between retrieving the whole and narrowing in on the particular, sometimes diverse, facts in a document becomes stronger. And that’s why this week we’ll learn one approach: late interaction!

-Doug

PS today, 12:30PM ET is the last day to sign up for Cheat at Search with Agents: http://maven.com/softwaredoug/cheat-at-search​

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