5 citations · 5 across the 5 of their papers we have counts for
5 papers
Adversarial Preference Learning for Robust LLM Alignment
Yuanfu Wang, Pengyu Wang, Chenyang Xi +13
Modern language models often rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors. However, they remain vulnerable to adversarial attacks due to th…
Token-level Accept or Reject: A Micro Alignment Approach for Large Language Models
Yang Zhang, Yu Yu, Bo Tang +8
With the rapid development of Large Language Models (LLMs), aligning these models with human preferences and values is critical to ensuring ethical and safe applications. However,…
Xinyu: An Efficient LLM-based System for Commentary Generation
Yiquan Wu, Bo Tang, Chenyang Xi +13
Commentary provides readers with a deep understanding of events by presenting diverse arguments and evidence. However, creating commentary is a time-consuming task, even for skille…
Controllable Text Generation for Large Language Models: A Survey
Xun Liang, Hanyu Wang, Yezhaohui Wang +8
In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world applications, LLMs must meet increasingly…
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy
Yu Zhu, Chuxiong Sun, Wenfei Yang +8
Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach to ensure Large Language Models (LLMs) align with human values. However, existing RLHF methods require…