arXiv:2607. 08034v1 Announce Type: cross Abstract: Large language models (LLMs) are used worldwide, yet disproportionately reflect Western values, limiting their ability to represent diverse value systems.
By Dhruv Agarwal, Anya Shukla, Tanya Goyal, Aditya Vashistha
arXiv:2608. 07367v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly used as a primary source of information and advice, understanding their alignment to humans in terms of values becomes a pressing concern.
By Maria-Louisa Wightman, Guillaume Bied, Tijl De Bie
arXiv:2607. 24782v1 Announce Type: new Abstract: LLM behavior may be conditioned by human identity in several ways: they may be asked to adapt to users, role-play populations, or forecast how people would answer value-laden questions.
By James Wedgwood, Pratiksha Thaker, Neil Kale, Virginia Smith
arXiv:2606. 00369v1 Announce Type: cross Abstract: Safe global deployment of AI models requires alignment with human values that vary across cultures.
By Arkadiy Saakyan, Charvi Rastogi, Lora Aroyo
arXiv:2606. 10569v1 Announce Type: cross Abstract: Standard RLHF pipelines often reduce heterogeneous human judgments into a single scalar reward target.
By Dorcas Chia Ern Chua, Karen Myn Hui Lee, Jia Yue Tan, Zhen Xue Gue, Norzalena Abdul Hamid, Azima Binti Azmi, Keat Mei Yeong, Aizat Izyani binti Mujab, Hafsah Noor Azam, Chee Guo Khoo, Han Ying Lim, Chee Seng Chan
arXiv:2602. 03160v2 Announce Type: replace Abstract: Aligning Large Language Models (LLMs) with the diverse spectrum of human values remains a central challenge: preference-based methods often fail to capture deeper motivational principles.
By Woojin Kim, Sieun Hyeon, Jusang Oh, Jaeyoung Do