63 citations · 118 across the 5 of their papers we have counts for
5 papers
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
Song Wang, Zihan Chen, Peng Wang +5
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to address their limitations in accessing up-to-date or special…
HYRR: Hybrid Infused Reranking for Passage Retrieval
Jing Lu, Keith Hall, Ji Ma +1
We present Hybrid Infused Reranking for Passages Retrieval (HYRR), a framework for training rerankers based on a hybrid of BM25 and neural retrieval models. Retrievers based on hyb…
Promptagator: Few-shot Dense Retrieval From 8 Examples
Zhuyun Dai, Vincent Y. Zhao, Ji Ma +7
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 lim…
Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lu +8
It has been shown that dual encoders trained on one domain often fail to generalize to other domains for retrieval tasks. One widespread belief is that the bottleneck layer of a du…
Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models
Jianmo Ni, Gustavo Hernández Ábrego, Noah Constant +4
We provide the first exploration of sentence embeddings from text-to-text transformers (T5). Sentence embeddings are broadly useful for language processing tasks. While T5 achieves…