1 citations · 1 across the 1 of their papers we have counts for
4 papers
MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization
Yougang Lyu, Lingyong Yan, Zihan Wang +4
As large language models (LLMs) are rapidly advancing and achieving near-human capabilities on specific tasks, aligning them with human values is becoming more urgent. In scenarios…
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator
Junda Zhu, Lingyong Yan, Haibo Shi +2
Large language models (LLMs) are proven to benefit a lot from retrieval-augmented generation (RAG) in alleviating hallucinations confronted with knowledge-intensive questions. RAG…
GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation
Wenjie Zhou, Zhenxin Ding, Xiaodong Zhang +3
Pre-trained language models have become an integral component of question-answering systems, achieving remarkable performance. However, for practical deployment, it is crucial to p…
The Real, the Better: Aligning Large Language Models with Online Human Behaviors
Guanying Jiang, Lingyong Yan, Haibo Shi +1
Large language model alignment is widely used and studied to avoid LLM producing unhelpful and harmful responses. However, the lengthy training process and predefined preference bi…