123 citations · 335 across the 26 of their papers we have counts for
6 papers · 1 filter
Chain-of-Retrieval Augmented Generation
Liang Wang, Haonan Chen, Nan Yang +3
This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer. Conventional R…
Fine-Tuning LLaMA for Multi-Stage Text Retrieval
Xueguang Ma, Liang Wang, Nan Yang +2
The effectiveness of multi-stage text retrieval has been solidly demonstrated since before the era of pre-trained language models. However, most existing studies utilize models tha…
Large Search Model: Redefining Search Stack in the Era of LLMs
Liang Wang, Nan Yang, Xiaolong Huang +3
Modern search engines are built on a stack of different components, including query understanding, retrieval, multi-stage ranking, and question answering, among others. These compo…
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…
Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval
Zehan Li, Nan Yang, Liang Wang +1
In this paper, we propose a new dense retrieval model which learns diverse document representations with deep query interactions. Our model encodes each document with a set of gene…
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval
Liang Wang, Nan Yang, Xiaolong Huang +5
In this paper, we propose SimLM (Similarity matching with Language Model pre-training), a simple yet effective pre-training method for dense passage retrieval. It employs a simple…