1 citations · 1 across the 4 of their papers we have counts for
6 papers
Modeling U.S. Attitudes Toward China via an Event-Steered Multi-Agent Simulator
Chenxu Zhu, Hantao Yao, Wu Liu +2
Understanding the dynamic evolution of opinions, such as U.S. public attitudes toward China, is essential for assessing geopolitical risks. However, existing LLM-based multiagent s…
Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models
Yi Liu, Dianqing Liu, Mingye Zhu +3
The widespread adoption of large language models (LLMs) across industries has increased the demand for high-quality and customizable outputs. However, traditional alignment methods…
Leveraging Robust Optimization for LLM Alignment under Distribution Shifts
Mingye Zhu, Yi Liu, Zheren Fu +2
Preference alignment methods are increasingly critical for steering large language models (LLMs) to generate outputs consistent with human values. While recent approaches often rel…
On-the-fly Preference Alignment via Principle-Guided Decoding
Mingye Zhu, Yi Liu, Lei Zhang +2
With the rapidly expanding landscape of large language models, aligning model generations with human values and preferences is becoming increasingly important. Popular alignment me…
FlipGuard: Defending Preference Alignment against Update Regression with Constrained Optimization
Mingye Zhu, Yi Liu, Quan Wang +2
Recent breakthroughs in preference alignment have significantly improved Large Language Models' ability to generate texts that align with human preferences and values. However, cur…
LIRE: listwise reward enhancement for preference alignment
Mingye Zhu, Yi Liu, Lei Zhang +2
Recently, tremendous strides have been made to align the generation of Large Language Models (LLMs) with human values to mitigate toxic or unhelpful content. Leveraging Reinforceme…