941 citations · 2.5k across the 22 of their papers we have counts for
22 papers
Learning to Retrieve In-Context Examples for Large Language Models
Liang Wang, Nan Yang, Furu Wei
Large language models (LLMs) have demonstrated their ability to learn in-context, allowing them to perform various tasks based on a few input-output examples. However, the effectiv…
Learning to Rank in Generative Retrieval
Yongqi Li, Nan Yang, Liang Wang +2
Generative retrieval stands out as a promising new paradigm in text retrieval that aims to generate identifier strings of relevant passages as the retrieval target. This generative…
Multiview Identifiers Enhanced Generative Retrieval
Yongqi Li, Nan Yang, Liang Wang +2
Instead of simply matching a query to pre-existing passages, generative retrieval generates identifier strings of passages as the retrieval target. At a cost, the identifier must b…
Inference with Reference: Lossless Acceleration of Large Language Models
Nan Yang, Tao Ge, Liang Wang +5
We propose LLMA, an LLM accelerator to losslessly speed up Large Language Model (LLM) inference with references. LLMA is motivated by the observation that there are abundant identi…
Query2doc: Query Expansion with Large Language Models
Liang Wang, Nan Yang, Furu Wei
This paper introduces a simple yet effective query expansion approach, denoted as query2doc, to improve both sparse and dense retrieval systems. The proposed method first generates…
Text Embeddings by Weakly-Supervised Contrastive Pre-training
Liang Wang, Nan Yang, Xiaolong Huang +5
This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. The model is trained in a contrastive manner with weak supervision…