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cs.CL2026
CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters
Ao Sun, Xiaoyu Wang, Zhe Tan +4
As Large Language Models (LLMs) serve a global audience, alignment must transition from enforcing universal consensus to respecting cultural pluralism. We demonstrate that dense mo…
cs.CL2026
The Need for a Socially-Grounded Persona Framework for User Simulation
Pranav Narayanan Venkit, Yu Li, Yada Pruksachatkun +1
Synthetic personas are widely used to condition large language models (LLMs) for social simulation, yet most personas are still constructed from coarse sociodemographic attributes…
cs.CL2024
Progressive-Hint Prompting Improves Reasoning in Large Language Models
Chuanyang Zheng, Zhengying Liu, Enze Xie +2
The performance of Large Language Models (LLMs) in reasoning tasks depends heavily on prompt design, with Chain-of-Thought (CoT) and self-consistency being critical methods that en…