MLn Club (ML Reading Group) #11: Fundamental Limitations of Single-Vector Embeddings
Welcome to Week 11: Fundamental Limitations of Single-Vector Embeddings
On the Theoretical Limitations of Embedding-Based Retrieval
Why does something as simple as "finding people who like apples" break our best models?
What do alternatives like multi-vector models or cross-encoders mean for real products?
This research establishes a fundamental mathematical constraint in dense retrieval: embedding models cannot represent all possible top-k combinations of relevant documents simultaneously.
Weller et al. demonstrate this through both theory and a clever benchmark (LIMIT) where even advanced state-of-the-art models struggle with trivially simple queries, suggesting important implications for instruction-following retrieval systems.
Join us at CASI for discussion at 8 pm, and (optional) quiet reading from 7 pm.
📖 Reading Recommendations, Questions, or Comments? Contact us here!
🔎 View past meeting notes here.
What's this?
A super warm group of folks discussing their favorite topics!
In the first half, we host an optional quiet reading space
In the second half, we have a discussion where people can talk about what they found interesting about the reading and ask questions about things they didn't understand
When/Where:
CMU AI Safety Initiative's Office, 201 Craig Street, right across the PNC bank. Look for the open door up the stairs.
8pm discussion, 7pm optional quiet reading time.
Here's how it usually goes:
7:00 PM — arrival and settling in
8:00 PM — introductions
8:10 PM — discussion time
9:00 PM — wrap up then open discussion
Who's it for?
People who've been wanting to read up on the latest papers in ML and other fields but just haven't been able to find the time/motivation.
Why:
We've been procrastinating too much on our readings, even though we have so much fun doing them. We know we're not alone in this and want to keep others accountable for learning more about what they're passionate about!
We've also met a ton of really fun friends by discussing what we care about!
Rules/guidelines on how to act:
Act like a host, include people in conversations, talk to people even if they're strangers, offer to explain what you know, and keep an open mind! come to read stuff and find super fun friends :)
Bring snacks if you're feeling kind!