activity
20232026
most citedHAMUR: Hyper Adapter for Multi-Domain Recommendation

51 citations · 71 across the 40 of their papers we have counts for

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Showing 2025Show all

12 papers · 1 filter

cs.CL2025

RoSA: Enhancing Parameter-Efficient Fine-Tuning via RoPE-aware Selective Adaptation in Large Language Models

Dayan Pan, Jingyuan Wang, Yilong Zhou +3

Fine-tuning large language models is essential for task-specific adaptation, yet it remains computationally prohibitive. Parameter-Efficient Fine-Tuning (PEFT) methods have emerged…

cs.CL2025

Learning a Single Token to Replace Long System Prompts in LLMs

Jiancheng Dong, Pengyue Jia, Jingyu Peng +7

Long system prompts are widely used to steer Large Language Models (LLMs), but repeatedly processing them at inference time is inefficient and consumes valuable context budget. Thi…

cs.IR2025

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…

cs.IR2025★ 1 cited

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…

cs.CL2025★ 1 cited

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…

cs.IR2025

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…

cs.CV2025

GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization

Pengyue Jia, Seongheon Park, Song Gao +2

Worldwide image geolocalization-the task of predicting GPS coordinates from images taken anywhere on Earth-poses a fundamental challenge due to the vast diversity in visual content…

cs.IR2025

LSRP: A Leader-Subordinate Retrieval Framework for Privacy-Preserving Cloud-Device Collaboration

Yingyi Zhang, Pengyue Jia, Xianneng Li +8

Cloud-device collaboration leverages on-cloud Large Language Models (LLMs) for handling public user queries and on-device Small Language Models (SLMs) for processing private user d…

cs.IR2025★ 2 cited

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…

cs.IR2025★ 1 cited

Joint Modeling in Recommendations: A Survey

Xiangyu Zhao, Yichao Wang, Bo Chen +7

In today's digital landscape, Deep Recommender Systems (DRS) play a crucial role in navigating and customizing online content for individual preferences. However, conventional meth…

cs.CL2025

TAPO: Task-Referenced Adaptation for Prompt Optimization

Wenxin Luo, Weirui Wang, Xiaopeng Li +3

Prompt engineering can significantly improve the performance of large language models (LLMs), with automated prompt optimization (APO) gaining significant attention due to the time…

cs.IR2025

SampleLLM: Optimizing Tabular Data Synthesis in Recommendations

Jingtong Gao, Zhaocheng Du, Xiaopeng Li +5

Tabular data synthesis is crucial in machine learning, yet existing general methods-primarily based on statistical or deep learning models-are highly data-dependent and often fall…