Andrew Drozdov

Andrew Drozdov

@mrdrozdov.com

Search and Agents @ Databricks

5.4K followers611 following185 posts

Andrew Drozdov reposted

The transformer was invented in Google. RLHF was not invented in industry labs, but came to prominence in OpenAI and DeepMind. I took 5 of the most influential papers (black dots) and visualized their references. Blue dots are papers that acknowledge federal funding (DARPA, NSF).

Andrew Drozdov reposted

We have updated #nocha, a leaderboard for reasoning over long-context narratives 📖, with some new models including #Gemini 2.5 Pro which shows massive improvements over the previous version! Congrats to #Gemini team 🪄 🧙 Check 🔗 novelchallenge.github.io for details :)

One, and two, and three police persons spring out of the shadows Down the corner comes one more And we scream into that city night: “three plus one makes four!” Well, they seem to think we’re disturbing the peace But we won’t let them make us sad ’Cause kids like you and me baby, we were born to add

We're probably a little too obsessed with zero-shot retrieval. If you have documents (you do), then you can generate synthetic data, and finetune your embedding. Blog post lead by @jacobianneuro.bsky.social shows how well this works in practice. www.databricks.com/blog/improvi...

databricks.comImproving Retrieval and RAG with Embedding Model FinetuningFine-tune embedding models on Databricks to enhance retrieval and RAG accuracy with synthetic data—no manual labeling required.

It was a real pleasure talking about effective IR approaches with Brooke and Denny on the Data Brew podcast. Among other things, I'm excited about embedding finetuning and reranking as modular ways to improve RAG pipelines. Everyone should use these more!

Search is the key to building trustworthy AI and will only be more important as we build more ambitious applications. With that in mind, there's not nearly enough energy spent improving the quality of search systems. Follow the link for the full episode: www.linkedin.com/posts/data-b...

linkedin.comData Brew by Databricks on LinkedIn: Join us on the latest Data Brew episode for a deep dive on Retrieval…Join us on the latest Data Brew episode for a deep dive on Retrieval, rerankers, and RAG tips and tricks with our very own Andrew Drozdov, Research Scientist…

It’s pretty obvious we’re in a local minima for pretraining. Would expect more breakthroughs in the 5-10 year range. Granted, it’s still incredibly hard and expensive to do good research in this space, despite the number of labs working on it.

kyunghyuncho.bsky.social@kyunghyuncho.bsky.social

"the gap between OAI/Anthropic/Meta/etc. and a large group of companies all over the world you've never cared to know of, in terms of LM pre-training? tiny" - 💡 me (Nov 2, 2024)

Andrew Drozdov reposted

Andrew Drozdov reposted

Andrew Drozdov reposted

Replying to @mrdrozdov.com

I’m being facetious, but the truth behind the joke is that OCR correction opens up the possibility (and futility) of language much like drafting poetry. For every interpreted pattern for optimizing OCR correction, exceptions arise. So, too, with patterns in poetry.

Andrew Drozdov reposted

Reflections on NeurIPS: There's always a big theme people seem to be preoccupied with. This year, it was the continuation of scaling/progress. Will it continue? What will the next generation of models hold? I even got to sass Dylan Patel (not on bsky) over it. Here are my personal thoughts 🧵

Slides are up! I presented on "Presentation & Consumption in the context of REML" The full deck is here. There's a lot of gems if you're interested in this space! retrieval-enhanced-ml.github.io/sigir-ap2024...

Andrew Drozdov@mrdrozdov.com

Today we'll be presenting the Tutorial on Retrieval-Enhanced Machine Learning (REML). Come by to learn about the emerging design patterns in this space and see how to use retrieval beyond RAG. In collaboration w/ the amazing @841io.bsky.social @teknology.bsky.social Alireza Salemi and Hamed Zamani.

Few things that are revolutionizing retrieval research right now: 1. content-based models have gotten much better 2. synthetic data has increased the value of small specialized datasets 3. retrieval is becoming a more important component in a variety of AI applications

2024: For really hard problems, you go to that one friend down the street who has an o1 pro subscription. 1960: For really important calls, you go to that one friend down the street who was able to get a telephone.

👀

Sumit@reachsumit.com

Down with the Hierarchy: The 'H' in HNSW Stands for "Hubs" Challenges the conventional wisdom by showing that hierarchical layers in HNSW are unnecessary for high-dimensional data. 📝 arxiv.org/abs/2412.01940 👨🏽‍💻 github.com/BlaiseMuhirw...

Somehow missed this thread from @sungkim.bsky.social --- thanks for the interest in our work!

Sung Kim@sungkim.bsky.social

Oh wow! A surprising result from Databricks when measuring embeddings and rerankers on internal evals. 1- Reranking few docs improves recall (expected). 2- Reranking many docs degrades quality (!). 3- Reranking too many documents is quite often worse than using embedding model alone (!!).