3 citations · 4 across the 8 of their papers we have counts for
9 papers
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…
Data, Not Model: Explaining Bias toward LLM Texts in Neural Retrievers
Wei Huang, Keping Bi, Yinqiong Cai +3
Recent studies show that neural retrievers often display source bias, favoring passages generated by LLMs over human-written ones, even when both are semantically similar. This bia…
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…