I'll have the pleasure this week to interview andrew drozdov senior research scientist at databricks on all things retrievals like: - RL for knowledge agents (see KARL) - learning representation pro...

Boost RAG agent quality with reranking—deliver more relevant answers in less time with a single parameter in Mosaic AI Vector Search.

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Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is limited, with the impli...

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…

Fine-tune embedding models on Databricks to enhance retrieval and RAG accuracy with synthetic data—no manual labeling required.

Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of n...

Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Among these, state spa...

SIGIR-AP 2024 Tutorial: Retrieval-Enhanced Machine Learning: Synthesis and Opportunities