14 citations · 39 across the 24 of their papers we have counts for
5 papers · 1 filter
SynerGen: Contextualized Generative Recommender for Unified Search and Recommendation
Vianne R. Gao, Chen Xue, Marc Versage +11
The dominant retrieve-then-rank pipeline in large-scale recommender systems suffers from mis-calibration and engineering overhead due to its architectural split and differing optim…
Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval
Yuxiang Liu, Tian Wang, Gourab Kundu +6
Transformer-based models such as BERT and E5 have significantly advanced text embedding by capturing rich contextual representations. However, many complex real-world queries requi…
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Han Xie, Da Zheng, Jun Ma +9
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be…
DynaMaR: Dynamic Prompt with Mask Token Representation
Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam +4
Recent research has shown that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. Typically wh…
Magic Pyramid: Accelerating Inference with Early Exiting and Token Pruning
Xuanli He, Iman Keivanloo, Yi Xu +4
Pre-training and then fine-tuning large language models is commonly used to achieve state-of-the-art performance in natural language processing (NLP) tasks. However, most pre-train…