AI RESEARCH
On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability
arXiv CS.CL
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ArXi:2604.16576v1 Announce Type: cross Decoder-only large language models (LLMs) are increasingly replacing BERT-style architectures as the backbone for dense retrieval, achieving substantial performance gains and broad adoption. However, the robustness of these LLM-based retrievers remains underexplored. In this paper, we present the first systematic study of the robustness of state-of-the-art open-source LLM-based dense retrievers from two complementary perspectives: generalizability and stability.