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
The paper investigates how large language models (LLMs) can exhibit stigma toward people with psychological conditions by examining their intermediate reasoning steps rather than just final answers. Using clinical expertise, the authors develop a framework to identify and rate stigmatizing language in LLM reasoning, distinguishing between overt prejudice and subtler biases. They also expand an existing mental health stigma benchmark to include more psychological conditions, finding that reasoning analysis reveals far more stigma than traditional multiple-choice evaluations and exposes flaws in the models’ logic and understanding of mental health.
By Sreehari Sankar, Aliakbar Nafar, Mona Barman, Hannah K. Heitz, Ashwin Kumar, Pouria Tohidi, Dailun Li, Danish Hussain, Russell DuBois, Hamed Hasheminia, Farshad Majzoubi
arXiv:2608. 05583v1 Announce Type: cross Abstract: As large language models (LLMs) enter high-stakes domains such as healthcare, understanding their moral reasoning becomes essential.
By Hadi Hosseini, Samarth Khanna, Leona Pierce
As large language models (LLMs) enter high-stakes domains such as healthcare, understanding their moral reasoning becomes essential. Decisions about scarce medical resources often hinge on judgments of responsibility, particularly when patients' own actions contribute to illness.
arXiv:2609.38036v1 Announce Type: cross
Abstract: Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support tools with...
By Edoardo Bolzoni, Valerio Capraro
arXiv:2609.38036v2 Announce Type: replace-cross
Abstract: Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support to...
By Edoardo Bolzoni, Valerio Capraro