2 citations · 2 across the 5 of their papers we have counts for
5 papers
MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation
Chuanjie Wu, Zhishang Xiang, Yunbo Tang +3
Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge. Although effecti…
Beyond the Flat Sequence: Hierarchical and Preference-Aware Generative Recommendations
Zerui Chen, Heng Chang, Tianying Liu +5
Generative Recommenders (GRs), exemplified by the Hierarchical Sequential Transduction Unit (HSTU), have emerged as a powerful paradigm for modeling long user interaction sequences…
Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues
Tao He, Lizi Liao, Yixin Cao +5
Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional t…
GUIDE: A Guideline-Guided Dataset for Instructional Video Comprehension
Jiafeng Liang, Shixin Jiang, Zekun Wang +7
There are substantial instructional videos on the Internet, which provide us tutorials for completing various tasks. Existing instructional video datasets only focus on specific st…
Planning Like Human: A Dual-process Framework for Dialogue Planning
Tao He, Lizi Liao, Yixin Cao +4
In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typic…