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Andrew Drozdov
This week I'll have a long talk with @yacinemahdid.bsky.social about KARL and all things retrieval, and couldn't be looking forward to it more. substack.com/@yacinelearn...
We built a thing! The Databricks Reranker is now in Public Preview. It's as easy as changing the arguments to your vector search call, and doesn't require any additional setup. Read more: www.databricks.com/blog/reranki...
Andrew Drozdov reposted
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 :)
Andrew Drozdov reposted
MLflow is on BlueSky! Follow @mlflow.org to keep up to date on new releases, blogs and tutorials, events, and more.
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...
Embedding finetuning is not a new idea, but it's still overlooked IMO. The promptagator work is one of the more impactful papers that show finetuning with synthetic data is effective. arxiv.org/abs/2209.11755
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...
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.
"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
... didn't know this would be one of the hottest takes i've had ... for more on my thoughts, see drive.google.com/file/d/1sk_t...
synthetic data is the weirdest name for data augmentation.
Andrew Drozdov reposted
feeling a but under the weather this week … thus an increased level of activity on social media and blog: kyunghyuncho.me/i-sensed-anx...
Andrew Drozdov reposted
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference Introduces ModernBERT, a bidirectional encoder advancing BERT-like models with 8K context length. 📝 arxiv.org/abs/2412.13663 👨🏽💻 github.com/AnswerDotAI/...
Andrew Drozdov reposted
State Space Models are Strong Text Rerankers Shows Mamba-based models achieve comparable reranking performance to transformers while being more memory efficient, with Mamba-2 outperforming Mamba-1. 📝 arxiv.org/abs/2412.14354
Andrew Drozdov reposted
Andrew Drozdov reposted
The future of AI is models that generate graphical interfaces. Instead of the linear, low-bandwidth metaphor of conversation, models will represent themselves to us as computers: rich visuals, direct manipulation, and instant feedback. willwhitney.com/computing-in...
Andrew Drozdov reposted
Three must read papers for PhD students. #scisky #PhD #science #research #academicsky 1. The importance of stupidity in scientific research Open Access journals.biologists.com/jcs/article/...
Andrew Drozdov reposted
Andrew Drozdov reposted
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...
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.
Andrew Drozdov reposted
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.
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.
I guess it's not an official ad. bsky.app/profile/why....
To be clear, this is not us. We dont know who is doing this.
Somehow missed this thread from @sungkim.bsky.social --- thanks for the interest in our work!
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 (!!).
Andrew Drozdov reposted
I’m on the academic job market this year! I’m completing my @uwcse.bsky.social @uwnlp.bsky.social Ph.D. (2025), focusing on overcoming LLM limitations like hallucinations, by building new LMs. My Ph.D. work focuses on Retrieval-Augmented LMs to create more reliable AI systems 🧵