@inproceedings{okite-etal-2025-benchmarking,
    title = "Benchmarking and Improving {LLM} Robustness for Personalized Generation",
    author = "Okite, Chimaobi  and
      Deng, Naihao  and
      Bodipati, Kiran  and
      Hou, Huaidian  and
      Chai, Joyce  and
      Mihalcea, Rada",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.870/",
    doi = "10.18653/v1/2025.findings-emnlp.870",
    pages = "16040--16072",
    ISBN = "979-8-89176-335-7",
    abstract = "Recent years have witnessed a growing interest in personalizing the responses of large language models (LLMs). While existing evaluations primarily focus on whether a response aligns with a user{'}s preferences, we argue that factuality is an equally important yet often overlooked dimension. In the context of personalization, we define a model as robust if its responses are both factually accurate and align with the user preferences. To assess this, we introduce PERG, a scalable framework for evaluating robustness of LLMs in personalization, along with a new dataset, PERGData. We evaluate fourteen models from five different model families using different prompting methods. Our findings show that current LLMs struggle with robust personalization: even the strongest models (GPT-4.1, LLaMA3-70B) fails to maintain correctness in 5{\%} of previously successful cases without personalization, while smaller models (e.g., 7B scale) can fail more than 20{\%} of the time. Further analysis reveals that robustness is significantly affected by the nature of the query and the type of user preference. To mitigate these failures, we propose Pref-Aligner, a two-stage approach that improves robustness by an average of 25{\%} across models. Our work highlights critical gaps in current evaluation practices and introduces tools and metrics to support more reliable, user-aligned LLM deployments."
}