2 citations · 5 across the 22 of their papers we have counts for
8 papers · 1 filter
MTA: A Merge-then-Adapt Framework for Personalized Large Language Model
Xiaopeng Li, Yuanjin Zheng, Wanyu Wang +6
Personalized Large Language Models (PLLMs) aim to align model outputs with individual user preferences, a crucial capability for user-centric applications. However, the prevalent a…
NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendations
Yejing Wang, Shengyu Zhou, Jinyu Lu +9
Generative Recommendation (GR), powered by Large Language Models (LLMs), represents a promising new paradigm for industrial recommender systems. However, their practical applicatio…
Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval
Yingyi Zhang, Pengyue Jia, Derong Xu +9
Retrieval-Augmented Generation (RAG) critically depends on effective query expansion to retrieve relevant information. However, existing expansion methods adopt uniform strategies…
Deep Research: A Survey of Autonomous Research Agents
Wenlin Zhang, Xiaopeng Li, Yingyi Zhang +5
The rapid advancement of large language models (LLMs) has driven the development of agentic systems capable of autonomously performing complex tasks. Despite their impressive capab…
GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
Yejing Wang, Shengyu Zhou, Jinyu Lu +9
Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scen…
From Single to Multi-Granularity: Toward Long-Term Memory Association and Selection of Conversational Agents
Derong Xu, Yi Wen, Pengyue Jia +8
Large Language Models (LLMs) have recently been widely adopted in conversational agents. However, the increasingly long interactions between users and agents accumulate extensive d…
Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
Wenlin Zhang, Xiangyang Li, Kuicai Dong +9
Retrieval-augmented generation (RAG) enhances the text generation capabilities of large language models (LLMs) by integrating external knowledge and up-to-date information. However…
A Survey of Personalization: From RAG to Agent
Xiaopeng Li, Pengyue Jia, Derong Xu +11
Personalization has become an essential capability in modern AI systems, enabling customized interactions that align with individual user preferences, contexts, and goals. Recent r…