565 citations · 581 across the 14 of their papers we have counts for
4 papers · 1 filter
MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers
Jushaan Singh Kalra, Xinran Zhao, To Eun Kim +3
Retrieval-augmented Generation (RAG) is powerful, but its effectiveness hinges on which retrievers we use and how. Different retrievers offer distinct, often complementary signals:…
Revela: Dense Retriever Learning via Language Modeling
Fengyu Cai, Tong Chen, Xinran Zhao +5
Dense retrievers play a vital role in accessing external and specialized knowledge to augment language models (LMs). Training dense retrievers typically requires annotated query-do…
: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity
Fengyu Cai, Xinran Zhao, Tong Chen +4
Recent studies show the growing significance of document retrieval in the generation of LLMs, i.e., RAG, within the scientific domain by bridging their knowledge gap. However, dens…
Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness
Xinran Zhao, Tong Chen, Sihao Chen +2
The task of Information Retrieval (IR) requires a system to identify relevant documents based on users' information needs. In real-world scenarios, retrievers are expected to not o…