On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability
For researchers and practitioners using LLM-based dense retrievers, this work provides critical insights into their robustness limitations and design principles for future models.
This paper presents the first systematic study of robustness in LLM-based dense retrievers, evaluating generalizability across 30 datasets and stability against query variations and adversarial attacks. Key findings include that instruction-tuned models excel but complex reasoning models suffer a 'specialization tax', and that LLM-based retrievers are more robust to typos and corpus poisoning than encoder-only baselines but remain vulnerable to semantic perturbations.
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 heterogeneity. Our analysis reveals that while instruction-tuned models generally excel, those optimized for complex reasoning often suffer a ``specialization tax,'' exhibiting limited generalizability in broader contexts. For stability, we assess model resilience against both unintentional query variations~(e.g., paraphrasing, typos) and malicious adversarial attacks~(e.g., corpus poisoning). We find that LLM-based retrievers show improved robustness against typos and corpus poisoning compared to encoder-only baselines, yet remain vulnerable to semantic perturbations like synonymizing. Further analysis shows that embedding geometry (e.g., angular uniformity) provides predictive signals for lexical stability and suggests that scaling model size generally improves robustness. These findings inform future robustness-aware retriever design and principled benchmarking. Our code is publicly available at https://github.com/liyongkang123/Robust_LLM_Retriever_Eval.