5 papers · 1 filter
Beyond "I cannot fulfill this request": Alleviating Rigid Rejection in LLMs via Label Enhancement
Ying Zhang, Congyu Qiao, Xin Geng +1
Large Language Models (LLMs) rely on safety alignment to obey safe requests while refusing harmful ones. However, traditional refusal mechanisms often lead to "rigid rejection," wh…
VRM: Teaching Reward Models to Understand Authentic Human Preferences
Biao Liu, Ning Xu, Junming Yang +2
Large Language Models (LLMs) have achieved remarkable success across diverse natural language tasks, yet the reward models employed for aligning LLMs often encounter challenges of…
Alignment through Meta-Weighted Online Sampling: Bridging the Gap between Data Generation and Preference Optimization
Junming Yang, Ning Xu, Biao Liu +2
Preference optimization is crucial for aligning large language models (LLMs) with human values and intentions. A significant challenge in this process is the distribution mismatch…
Preference Orchestrator: Prompt-Aware Multi-Objective Alignment for Large Language Models
Biao Liu, Ning Xu, Junming Yang +1
While Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks, aligning these models with varying human preferences…
Progressively Label Enhancement for Large Language Model Alignment
Biao Liu, Ning Xu, Xin Geng
Large Language Models (LLM) alignment aims to prevent models from producing content that misaligns with human expectations, which can lead to ethical and legal concerns. In the las…