arXiv AI
Evaluating the Hidden Costs of Personalization in Large Language Models
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Re-Centering Humans in LLM Personalization
arXiv:2606. 06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data.
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Tailored to you: longitudinal effects of personalising language models
The study examined how personalising language models affects user interactions over five days, comparing a non‑personalised baseline with memory‑based and survey‑based personalisation. Results showed that many interaction changes were due to repeated exposure, but personalisation influenced specific behaviors: memory‑based users disclosed more and found the model less creepy, while survey‑based users felt more regret about sharing personal data. The authors emphasize the nuanced, approach‑specific impacts on user attitudes and the need for careful design of personalised AI.
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Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions
arXiv:2603.04191v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactio...
arXiv Computation and Language
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PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information?
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arXiv AI
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The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs
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HyperTrace: Hypothesis-Based Preference Tracing for Online LLM Personalization
HyperTrace is a training‑free framework that personalizes large language models by tracing latent user preferences online. It maintains interpretable natural‑language hypotheses about short‑term intent and long‑term preferences, updating them with an SMC‑style reweighting process driven by an LLM‑based surrogate choice model. Experiments on PRISM and PersonaMem‑v2 demonstrate that HyperTrace improves response alignment, preference prediction, and profile consistency compared to strong online baselines.