2 citations · 3 across the 10 of their papers we have counts for
4 papers · 2 filters
Mitigating Biases in Language Models via Bias Unlearning
Dianqing Liu, Yi Liu, Guoqing Jin +1
Many studies have shown various biases targeting different demographic groups in language models, amplifying discrimination and harming fairness. Recent parameter modification debi…
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…