A State-of-the-Art Generalist Embedding Model, Bridging the Gap Between Single and Multi-Vector Retrieval, and More!

Vol.54 for May 27 - Jun 02, 2024

Stay Ahead of the Curve with the Latest Advancements and Discoveries in Information Retrieval.

This week’s newsletter highlights the following research:

1. Graph Retrieval-Augmented Generation for Context-Aware Reasoning, from Emory University

2. Enabling Fine-Grained Ranking at Varying Granularities via Multi-Vector Embeddings, from Reddy et al.

3. Learning to Ask Clarification Questions for Ambiguous Retrieval Queries, from UC Berkeley

4. Principled and Practical Multi-Vector Retrieval with Approximation Guarantees, from Google Research

5. Towards User-Centric Recommendations via LLM-Based Simulation and Tool Learning, from USTC

6. Building Pseudo-Graph Databases from Raw Texts for Retrieval-Augmented Language Models, from Renmin University

7. Retrieval-Augmented Early Exiting for Efficient Large Language Model Inference, from Huang et al.

8. A State-of-the-Art Generalist Embedding Model, from NVIDIA

9. Enhancing Multilingual Language Model Training via Repeat Ranking, from Devine et al.

10. A Real-World Evaluation of HNSW for Vector Retrieval, from Marqo

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