51 citations · 137 across the 11 of their papers we have counts for
17 papers
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation
Krishna Srinivasan, Karthik Raman, Anupam Samanta +3
Large Language Models (LLMs) have shown impressive results on a variety of text understanding tasks. Search queries though pose a unique challenge, given their short-length and lac…
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Honglei Zhuang, Zhen Qin, Rolf Jagerman +6
Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT. However, there are limited studies on how to leverage more powerful s…
Retrieval Augmentation for T5 Re-ranker using External Sources
Kai Hui, Tao Chen, Zhen Qin +4
Retrieval augmentation has shown promising improvements in different tasks. However, whether such augmentation can assist a large language model based re-ranker remains unclear. We…
Retrieval-Enhanced Machine Learning
Hamed Zamani, Fernando Diaz, Mostafa Dehghani +2
Although information access systems have long supported people in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to inc…
Dynamic Language Models for Continuously Evolving Content
Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang +2
The content on the web is in a constant state of flux. New entities, issues, and ideas continuously emerge, while the semantics of the existing conversation topics gradually shift.…
LAMPRET: Layout-Aware Multimodal PreTraining for Document Understanding
Te-Lin Wu, Cheng Li, Mingyang Zhang +3
Document layout comprises both structural and visual (eg. font-sizes) information that is vital but often ignored by machine learning models. The few existing models which do use l…