9 papers
Hypencoder Revisited: Reproducibility and Analysis of Non-Linear Scoring for First-Stage Retrieval
Arne Eichholtz, Yongkang Li, Jutte Vijverberg +2
The Hypencoder, proposed by Killingback et al., is a retrieval framework that replaces the fixed inner-product scoring function used in standard bi-encoders with a query-specific n…
Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval
Kidist Amde Mekonnen, Yongkang Li, Yubao Tang +2
Generative retrieval (GR) ranks documents by autoregressively generating document identifiers. Because many GR methods rely on trie-constrained beam search, they are vulnerable to…
On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability
Yongkang Li, Panagiotis Eustratiadis, Yixing Fan +1
Decoder-only large language models (LLMs) are increasingly replacing BERT-style architectures as the backbone for dense retrieval, achieving substantial performance gains and broad…
Spectral Tempering for Embedding Compression in Dense Passage Retrieval
Yongkang Li, Panagiotis Eustratiadis, Evangelos Kanoulas
Dimensionality reduction is critical for deploying dense retrieval systems at scale, yet mainstream post-hoc methods face a fundamental trade-off: principal component analysis (PCA…
Unsupervised Corpus Poisoning Attacks in Continuous Space for Dense Retrieval
Yongkang Li, Panagiotis Eustratiadis, Simon Lupart +1
This paper concerns corpus poisoning attacks in dense information retrieval, where an adversary attempts to compromise the ranking performance of a search algorithm by injecting a…
Multifaceted Scenario-Aware Hypergraph Learning for Next POI Recommendation
Yuxi Lin, Yongkang Li, Jie Xing +1
Among the diverse services provided by Location-Based Social Networks (LBSNs), Next Point-of-Interest (POI) recommendation plays a crucial role in inferring user preferences from h…