LLM-based dense retrievers generalize better when instruction-tuned but pay a specialization tax when optimized for reasoning; they resist typos and corpus poisoning better than encoder-only baselines yet remain vulnerable to semantic perturbations, with larger models and certain embedding geometry,
In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Informa- tion Retrieval
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Heuristic demonstration selection methods outperform embedding-based methods for practical LLM-based next POI prediction on three real-world datasets.
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On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability
LLM-based dense retrievers generalize better when instruction-tuned but pay a specialization tax when optimized for reasoning; they resist typos and corpus poisoning better than encoder-only baselines yet remain vulnerable to semantic perturbations, with larger models and certain embedding geometry,
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A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction
Heuristic demonstration selection methods outperform embedding-based methods for practical LLM-based next POI prediction on three real-world datasets.