8 citations · 17 across the 13 of their papers we have counts for
6 papers · 1 filter
LightRetriever: A LLM-based Text Retrieval Architecture with Extremely Faster Query Inference
Guangyuan Ma, Yongliang Ma, Xuanrui Gou +3
Large Language Models (LLMs)-based text retrieval retrieves documents relevant to search queries based on vector similarities. Documents are pre-encoded offline, while queries arri…
Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval
Guangyuan Ma, Yongliang Ma, Xing Wu +3
Large Language Model-based Dense Retrieval (LLM-DR) optimizes over numerous heterogeneous fine-tuning collections from different domains. However, the discussion about its training…
Drop your Decoder: Pre-training with Bag-of-Word Prediction for Dense Passage Retrieval
Guangyuan Ma, Xing Wu, Zijia Lin +1
Masked auto-encoder pre-training has emerged as a prevalent technique for initializing and enhancing dense retrieval systems. It generally utilizes additional Transformer decoder b…
Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval
Guangyuan Ma, Xing Wu, Peng Wang +2
In this paper, we systematically study the potential of pre-training with Large Language Model(LLM)-based document expansion for dense passage retrieval. Concretely, we leverage th…
PUNR: Pre-training with User Behavior Modeling for News Recommendation
Guangyuan Ma, Hongtao Liu, Xing Wu +4
News recommendation aims to predict click behaviors based on user behaviors. How to effectively model the user representations is the key to recommending preferred news. Existing w…
Query-as-context Pre-training for Dense Passage Retrieval
Xing Wu, Guangyuan Ma, Wanhui Qian +2
Recently, methods have been developed to improve the performance of dense passage retrieval by using context-supervised pre-training. These methods simply consider two passages fro…