6 citations · 9 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
Zhengliang Shi, Shen Gao, Lingyong Yan +6
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, enabling them to solve practical tasks. Previous methods ma…
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…
Learning to Use Tools via Cooperative and Interactive Agents
Zhengliang Shi, Shen Gao, Xiuyi Chen +7
Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. Existing methods employ one single LLM-based agent to iteratively sele…
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
Yougang Lyu, Lingyong Yan, Shuaiqiang Wang +6
Despite their success at many natural language processing (NLP) tasks, large language models still struggle to effectively leverage knowledge for knowledge-intensive tasks, manifes…