14 citations · 20 across the 4 of their papers we have counts for
5 papers
Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning
Marwa Abdulhai, Ryan Cheng, Donovan Clay +3
Large Language Models (LLMs) are increasingly used to simulate human users in interactive settings such as therapy, education, and social role-play. While these simulations enable…
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models
Mickel Liu, Liwei Jiang, Yancheng Liang +4
Conventional large language model (LLM) safety alignment relies on a reactive, disjoint loop: attackers exploit a static model, then defenders patch exposed vulnerabilities. This s…
Human Decision-making is Susceptible to AI-driven Manipulation
Sahand Sabour, June M. Liu, Siyang Liu +13
AI systems are increasingly intertwined with daily life, assisting users with various tasks and guiding decision-making. This integration introduces risks of AI-driven manipulation…
PERSONA: A Reproducible Testbed for Pluralistic Alignment
Louis Castricato, Nathan Lile, Rafael Rafailov +2
The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the…
A Roadmap to Pluralistic Alignment
Taylor Sorensen, Jared Moore, Jillian Fisher +9
With increased power and prevalence of AI systems, it is ever more critical that AI systems are designed to serve all, i.e., people with diverse values and perspectives. However, a…