arXiv:2606. 08483v1 Announce Type: new Abstract: Background: Consumer-facing large language models are now a common source of health information, and they interpret and personalize responses rather than retrieve them.
By Rahul Gorijavolu, Kaushik Madapati, Pritika Vig, Rawan Abulibdeh, Nikhil Jaiswal, Mahri Kadyrova, Zeamanuel Hailu Tesfaye, Charles Senteio, Paula Maurutto, Leo Anthony Celi
arXiv:2603.04299v5 Announce Type: replace
Abstract: LLMs often exhibit highly agreeable conversational styles, also known as AI sycophancy. This pattern may become problematic when interacting with u...
By Angelica Henestrosa, Zeyi Lu, Pavel Chizhov, Ivan P. Yamshchikov
arXiv:2606. 23884v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) are increasingly used for mental health-related conversations, yet safety safeguards remain inadequate and inconsistent across clinical conditions.
By Annika Marie Schoene, Cansu Canca, Gautham Vijay Kumar, Anson Antony
arXiv:2606. 26106v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in emotionally charged situations involving interpersonal conflict, frustration, and distress.
By Zhixing Sun, Shenghe Xu, Tao Li
The paper introduces Intent‑Aware Prompting (IAP), a new method that uses large language models to detect mental manipulation in conversations by identifying the underlying intents of participants. Experiments on the MentalManip dataset show that IAP outperforms other prompting strategies, especially by reducing false negatives and improving detection of subtle manipulative tactics. The authors provide the code for reproducibility.
By Jiayuan Ma, Hongbin Na, Zimu Wang, Yining Hua, Yue Liu, Wei Wang, Ling Chen
arXiv:2606. 18258v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries.
By Sunnie S. Y. Kim, Margit Bowler, Leon A Gatys