On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability
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. For generalizability, we evaluate retrieval effectiveness across four benchmarks spanning 30 datasets, using linear mixed-effects models to estimate marginal mean performance and disentangle intrinsic model capability from dataset...
| Authors | Yongkang Li, Panagiotis Eustratiadis, Yixing Fan et al. |
|---|---|
| Primary category | cs.IR |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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