4 papers · 1 filter
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…
Continual Learning for Generative Retrieval over Dynamic Corpora
Jiangui Chen, Ruqing Zhang, Jiafeng Guo +4
Generative retrieval (GR) directly predicts the identifiers of relevant documents (i.e., docids) based on a parametric model. It has achieved solid performance on many ad-hoc retri…
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…
Generative Retrieval Meets Multi-Graded Relevance
Yubao Tang, Ruqing Zhang, Jiafeng Guo +3
Generative retrieval represents a novel approach to information retrieval. It uses an encoder-decoder architecture to directly produce relevant document identifiers (docids) for qu…