Weize Kong
Weize Kong is a staff research scientist at Google DeepMind, working on large language models with a focus on knowledge augmentation (RAG & long context models) and personalization. Before joining Google, he received his Ph.D. at University of Massachusetts Amherst in the Center for Intelligent Information Retrieval (CIIR). Prior to CIIR, he worked as an undergraduate research assistant in the Information Retrieval Group at Tsinghua University.
Please see his personal homepage for a completed list of publications.
Authored Publications
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Bridging the Preference Gap between Retrievers and LLMs
Zixuan Ke
Qiaozhu Mei
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (2024) (to appear)
Learning to Rewrite Prompts for Personalized Text Generation
Qiaozhu Mei
Proceedings of the ACM Web Conference 2024
PRewrite: Prompt Rewriting with Reinforcement Learning
Qiaozhu Mei
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (2024) (to appear)
Learning Sparse Lexical Representations Over Expanded Vocabularies for Retrieval
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23) (2023)
End-to-End Query Term Weighting
Karan Samel
Tao Chen
Swaraj Khadanga
Wensong Xu
Xingyu Wang
Kashyap Kolipaka
Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '23) (2023)
SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23), ACM (2023) (to appear)
Multi-Aspect Dense Retrieval
Swaraj Khadanga
Wensong Xu
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM (2022)
Natural Language Understanding with Privacy-Preserving BERT
Proceedings of the 30th ACM International Conference on Information and Knowledge Management, ACM (2021)
Improving Cloud Storage Search with User Activity
Proceedings of the 14th International Conference on Web Search and Data Mining (WSDM '21), ACM (2021)
Learning to Cluster Documents into Workspaces Using Large Scale Activity Logs
Mike Colagrosso
Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’20), ACM (2020), 2416–2424
