arXiv AI By Ryuichi Sumida, Mao Saeki, Masaki Eguchi, Sadahiro Yoshikawa, Koji Inoue, Tatsuya Kawahara, Yoichi Matsuyama

Memory-Driven Self-Disclosure and Relational Turning Points: A Longitudinal Multimodal Study of Human-AI Interaction

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arXiv:2607. 14593v1 Announce Type: cross Abstract: As conversational AI systems are designed for repeated use, a central question is how a series of interactions becomes a relationship.

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arXiv AI
Sep 18

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.

By Canfer Akbulut, Justine Breuch, Arianna Manzini, Lujain Ibrahim, Matija Franklin, Roma Patel, Iason Gabriel, Kristian Lum, Laura Weidinger