@inproceedings{wang-etal-2026-evaluating-hidden,
title = "Evaluating the Hidden Costs of Personalization in Large Language Models",
author = "Wang, Yumeng and
Wu, Yuchen and
Qian, Cheng and
Fan, Zhiyuan and
Ha, Hyeonjeong and
Wu, Shujin and
Liu, Jiayu and
Ji, Heng and
Wang, Ge",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.679/",
pages = "15641--15665",
ISBN = "979-8-89176-459-0",
abstract = "While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing balanced, informative responses toward optimizing for user satisfaction when conditioned on personal context such as conversation history, inferred preferences, and user profiles. Specifically, we identify three emerging risks: (1) irrelevant personalization, where models reference personal information in unnecessary contexts; (2) preference narrowing, where models reinforce informational echo chambers; and (3) sycophantic bias, where models agree excessively with user opinions. As a result, models may reference personal information in contexts where it is unnecessary, inadvertently collapse response diversity, or agree excessively with user opinions. Despite the growing use of personalization in AI assistants, there has been limited systematic evaluation of its potential side effects. To bridge this gap, we propose PRISK, a dynamic evaluation framework with automated data generation and tailored metrics that uncovers systematic limitations in current LLM personalization and how personalized information shapes its responses. Our empirical analysis across 13 LLMs demonstrates that the presence of user profiles and retrieved memory consistently exacerbates biases, resulting in an average drop of 45.9{\%} in irrelevant personalization, 41.7{\%} in preference narrowing and 61.7{\%} in sycophantic bias."
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<abstract>While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing balanced, informative responses toward optimizing for user satisfaction when conditioned on personal context such as conversation history, inferred preferences, and user profiles. Specifically, we identify three emerging risks: (1) irrelevant personalization, where models reference personal information in unnecessary contexts; (2) preference narrowing, where models reinforce informational echo chambers; and (3) sycophantic bias, where models agree excessively with user opinions. As a result, models may reference personal information in contexts where it is unnecessary, inadvertently collapse response diversity, or agree excessively with user opinions. Despite the growing use of personalization in AI assistants, there has been limited systematic evaluation of its potential side effects. To bridge this gap, we propose PRISK, a dynamic evaluation framework with automated data generation and tailored metrics that uncovers systematic limitations in current LLM personalization and how personalized information shapes its responses. Our empirical analysis across 13 LLMs demonstrates that the presence of user profiles and retrieved memory consistently exacerbates biases, resulting in an average drop of 45.9% in irrelevant personalization, 41.7% in preference narrowing and 61.7% in sycophantic bias.</abstract>
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%0 Conference Proceedings
%T Evaluating the Hidden Costs of Personalization in Large Language Models
%A Wang, Yumeng
%A Wu, Yuchen
%A Qian, Cheng
%A Fan, Zhiyuan
%A Ha, Hyeonjeong
%A Wu, Shujin
%A Liu, Jiayu
%A Ji, Heng
%A Wang, Ge
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-evaluating-hidden
%X While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing balanced, informative responses toward optimizing for user satisfaction when conditioned on personal context such as conversation history, inferred preferences, and user profiles. Specifically, we identify three emerging risks: (1) irrelevant personalization, where models reference personal information in unnecessary contexts; (2) preference narrowing, where models reinforce informational echo chambers; and (3) sycophantic bias, where models agree excessively with user opinions. As a result, models may reference personal information in contexts where it is unnecessary, inadvertently collapse response diversity, or agree excessively with user opinions. Despite the growing use of personalization in AI assistants, there has been limited systematic evaluation of its potential side effects. To bridge this gap, we propose PRISK, a dynamic evaluation framework with automated data generation and tailored metrics that uncovers systematic limitations in current LLM personalization and how personalized information shapes its responses. Our empirical analysis across 13 LLMs demonstrates that the presence of user profiles and retrieved memory consistently exacerbates biases, resulting in an average drop of 45.9% in irrelevant personalization, 41.7% in preference narrowing and 61.7% in sycophantic bias.
%U https://aclanthology.org/2026.emnlp-main.679/
%P 15641-15665
Markdown (Informal)
[Evaluating the Hidden Costs of Personalization in Large Language Models](https://aclanthology.org/2026.emnlp-main.679/) (Wang et al., EMNLP 2026)
ACL
- Yumeng Wang, Yuchen Wu, Cheng Qian, Zhiyuan Fan, Hyeonjeong Ha, Shujin Wu, Jiayu Liu, Heng Ji, and Ge Wang. 2026. Evaluating the Hidden Costs of Personalization in Large Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15641–15665, Budapest, Hungary. Association for Computational Linguistics.