117 citations · 123 across the 11 of their papers we have counts for
11 papers
From Ranked Documents to Reliable Contexts: An Answer-Oriented Context Construct Framework for AI Search
Yunfei Zhong, Yinqiong Cai, Lixin Su +7
Traditional Web search follows a human-facing paradigm in which users inspect ranked documents and synthesize information themselves. In AI Search, retrieved documents instead serv…
Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation
Yunfei Zhong, Jun Yang, Wei Huang +7
A deployable multilingual reranker must not only generalize across languages, domains, and ranking tasks, but also remain efficient to serve as a second-stage reranker in practical…
Relevance Supervision Can Teach Neural Retrievers to Prefer LLM-Generated Text
Wei Huang, Keping Bi, Yinqiong Cai +3
Recent studies show that neural retrievers often exhibit source bias, ranking LLM-generated passages above semantically similar human-written ones, which threatens the reliability…
Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning
Jiahan Chen, Da Li, Hengran Zhang +6
Multimodal embedding models, rooted in multimodal large language models (MLLMs), have yielded significant performance improvements across diverse tasks such as retrieval and classi…
How Do LLM-Generated Texts Impact Term-Based Retrieval Models?
Wei Huang, Keping Bi, Yinqiong Cai +3
As more content generated by large language models (LLMs) floods into the Internet, information retrieval (IR) systems now face the challenge of distinguishing and handling a blend…
CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval
Yinqiong Cai, Yixing Fan, Keping Bi +4
The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently. Since various matching patterns can exist betwee…
L^2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations
Yinqiong Cai, Keping Bi, Yixing Fan +3
First-stage retrieval is a critical task that aims to retrieve relevant document candidates from a large-scale collection. While existing retrieval models have achieved impressive…
Hard Negatives or False Negatives: Correcting Pooling Bias in Training Neural Ranking Models
Yinqiong Cai, Jiafeng Guo, Yixing Fan +3
Neural ranking models (NRMs) have become one of the most important techniques in information retrieval (IR). Due to the limitation of relevance labels, the training of NRMs heavily…
Pre-training Methods in Information Retrieval
Yixing Fan, Xiaohui Xie, Yinqiong Cai +5
The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need. In r…
A Discriminative Semantic Ranker for Question Retrieval
Yinqiong Cai, Yixing Fan, Jiafeng Guo +3
Similar question retrieval is a core task in community-based question answering (CQA) services. To balance the effectiveness and efficiency, the question retrieval system is typica…
Semantic Models for the First-stage Retrieval: A Comprehensive Review
Jiafeng Guo, Yinqiong Cai, Yixing Fan +3
Multi-stage ranking pipelines have been a practical solution in modern search systems, where the first-stage retrieval is to return a subset of candidate documents, and latter stag…