123 citations · 218 across the 19 of their papers we have counts for
4 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…
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…
LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval
Tao Shen, Xiubo Geng, Chongyang Tao +5
In large-scale retrieval, the lexicon-weighting paradigm, learning weighted sparse representations in vocabulary space, has shown promising results with high quality and low latenc…
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…