7 papers
Learning Robust Personalized Prompts for LLM-Driven Sequential Recommendation
Xiaolin Zheng, Qiyong Zhong, Jiajie Su +1
LLM-driven sequential recommendation formulates next-item prediction as autoregressive generation conditioned on natural-language prompts. However, minor wording changes in semanti…
Is Serving-Time Language-Model Inference Necessary for Recommendation? Evidence from Generative and Agentic Settings
Kong Wang, Zhongke He, Xiang Chen +4
Language models (LMs) are increasingly used in recommender systems to generate recommendations or coordinate recommendation tools. However, does their strong performance imply that…
MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization
Md Mehrab Tanjim, Jayakumar Subramanian, Xiang Chen +6
LLM agents organize behavior through skills -- structured natural-language specifications that govern how an agent reasons, retrieves, and responds. Unlike monolithic prompts, skil…
Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation
Sixu Chen, Xiang Chen, Hongyao Yu +5
The widespread deployment and redistribution of large language models (LLMs) have made model provenance tracking a critical challenge. While existing LLM fingerprinting methods, pa…
From Selection to Generation: A Survey of LLM-based Active Learning
Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie +31
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent…
Personalized Multimodal Large Language Models: A Survey
Junda Wu, Hanjia Lyu, Yu Xia +24
Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as tex…
X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation
Hanjia Lyu, Ryan Rossi, Xiang Chen +4
Large Language Models (LLMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems. However, most ex…