8 citations · 10 across the 7 of their papers we have counts for
5 papers
KwaiYiiMath: Technical Report
Jiayi Fu, Lei Lin, Xiaoyang Gao +18
Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on math…
Confidence-based Visual Dispersal for Few-shot Unsupervised Domain Adaptation
Yizhe Xiong, Hui Chen, Zijia Lin +2
Unsupervised domain adaptation aims to transfer knowledge from a fully-labeled source domain to an unlabeled target domain. However, in real-world scenarios, providing abundant lab…
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…
CoT-MAE v2: Contextual Masked Auto-Encoder with Multi-view Modeling for Passage Retrieval
Xing Wu, Guangyuan Ma, Peng Wang +4
Growing techniques have been emerging to improve the performance of passage retrieval. As an effective representation bottleneck pretraining technique, the contextual masked auto-e…
ConTextual Masked Auto-Encoder for Dense Passage Retrieval
Xing Wu, Guangyuan Ma, Meng Lin +3
Dense passage retrieval aims to retrieve the relevant passages of a query from a large corpus based on dense representations (i.e., vectors) of the query and the passages. Recent s…