871 citations · 2.9k across the 70 of their papers we have counts for
6 papers · 1 filter
Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs
Ye Liu, Semih Yavuz, Rui Meng +4
The integration of retrieved passages and large language models (LLMs), such as ChatGPTs, has significantly contributed to improving open-domain question answering. However, there…
Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training
Ziwei Fan, Zhiwei Liu, Shelby Heinecke +4
Existing recommender systems face difficulties with zero-shot items, i.e. items that have no historical interactions with users during the training stage. Though recent works extra…
Generating Negative Samples for Sequential Recommendation
Yongjun Chen, Jia Li, Zhiwei Liu +4
To make Sequential Recommendation (SR) successful, recent works focus on designing effective sequential encoders, fusing side information, and mining extra positive self-supervisio…
RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems
Zohreh Ovaisi, Shelby Heinecke, Jia Li +3
Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender s…
Dense Hierarchical Retrieval for Open-Domain Question Answering
Ye Liu, Kazuma Hashimoto, Yingbo Zhou +3
Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA), where latent representations of questions and passages are exploited for maximum…
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approache…