75 citations · 217 across the 23 of their papers we have counts for
7 papers · 1 filter
From Matching to Generation: A Survey on Generative Information Retrieval
Xiaoxi Li, Jiajie Jin, Yujia Zhou +4
Information Retrieval (IR) systems are crucial tools for users to access information, which have long been dominated by traditional methods relying on similarity matching. With the…
A Multi-Task Embedder For Retrieval Augmented LLMs
Peitian Zhang, Shitao Xiao, Zheng Liu +2
LLMs confront inherent limitations in terms of its knowledge, memory, and action. The retrieval augmentation stands as a vital mechanism to address these limitations, which brings…
Generative Retrieval via Term Set Generation
Peitian Zhang, Zheng Liu, Yujia Zhou +3
Recently, generative retrieval emerges as a promising alternative to traditional retrieval paradigms. It assigns each document a unique identifier, known as DocID, and employs a ge…
Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval
Peitian Zhang, Zheng Liu, Shitao Xiao +2
Inverted file structure is a common technique for accelerating dense retrieval. It clusters documents based on their embeddings; during searching, it probes nearby clusters w.r.t.…
Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer
Yujia Zhou, Jing Yao, Zhicheng Dou +3
Document retrieval has been extensively studied within the index-retrieve framework for decades, which has withstood the test of time. Unfortunately, such a pipelined framework lim…
GateFormer: Speeding Up News Feed Recommendation with Input Gated Transformers
Peitian Zhang, Zheng liu
News feed recommendation is an important web service. In recent years, pre-trained language models (PLMs) have been intensively applied to improve the recommendation quality. Howev…
Learning to Select Historical News Articles for Interaction based Neural News Recommendation
Peitian Zhang, Zhicheng Dou, Jing Yao
The key to personalized news recommendation is to match the user's interests with the candidate news precisely and efficiently. Most existing approaches embed user interests into a…