2 citations · 2 across the 5 of their papers we have counts for
5 papers
ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval
Suyuan Huang, Chao Zhang, Yuanyuan Wu +12
Dense retrieval in most industries employs dual-tower architectures to retrieve query-relevant documents. Due to online deployment requirements, existing real-world dense retrieval…
Hierarchical Skip Decoding for Efficient Autoregressive Text Generation
Yunqi Zhu, Xuebing Yang, Yuanyuan Wu +1
Autoregressive decoding strategy is a commonly used method for text generation tasks with pre-trained language models, while early-exiting is an effective approach to speedup the i…
Parameter-Efficient Fine-Tuning with Layer Pruning on Free-Text Sequence-to-Sequence Modeling
Yunqi Zhu, Xuebing Yang, Yuanyuan Wu +1
The increasing size of language models raises great research interests in parameter-efficient fine-tuning such as LoRA that freezes the pre-trained model, and injects small-scale t…
Leveraging Summary Guidance on Medical Report Summarization
Yunqi Zhu, Xuebing Yang, Yuanyuan Wu +1
This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50K, 16K and 378K pairs of report and summary that are derive…
Differentiable N-gram Objective on Abstractive Summarization
Yunqi Zhu, Xuebing Yang, Yuanyuan Wu +2
ROUGE is a standard automatic evaluation metric based on n-grams for sequence-to-sequence tasks, while cross-entropy loss is an essential objective of neural network language model…