Publications

CAGS: Context-Aware Document Ranking with Contrastive Graph Sampling

Published in TKDE, 2024

[TKDE 2025]

Recommended citation: Z. Huang, Z. Dou, Y. Zhu and J. -R. Wen, CAGS: Context-Aware Document Ranking with Contrastive Graph Sampling, in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2024.3491996. keywords: {Data augmentation;Context modeling;Data models;Search engines;Contrastive learning;Training;Search problems;Encoding;Bidirectional control;Predictive models;Context-aware document ranking;contrastive learning;graph sampling} https://ieeexplore.ieee.org/document/10742917

One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models

Published in Arxiv, 2024

[Arxiv-2405]

Recommended citation: Yutao Zhu and Zhaoheng Huang and Zhicheng Dou and Ji-Rong Wen. 2024. One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models. Work in Progress. https://arxiv.org/abs/2405.19670

MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling

Published in Findings of EMNLP, 2022

EMNLP 2022 received 3964 papers and finally accepted 828 main conference papers (20.91%) and 548 findings (13.85%). 34.76% papers were accepted in total.

Recommended citation: Zhaoheng Huang, Zhicheng Dou, Yutao Zhu, and Zhengyi Ma. 2022. MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1030–1042, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. https://aclanthology.org/2022.findings-emnlp.73/