6 papers
MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs
He Li, Haoang Chi, Qizhou Wang +6
Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific co…
CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
Haohua Niu, Xingtong Yu, Yang Liu +6
Graph learning under distribution shift presents a persistent challenge, where models adapt to new graphs with limited or even no supervision. Recent graph--LLM approaches move tow…
Adaptive Graph Integration for Cross-Domain Recommendation via Heterogeneous Graph Coordinators
Hengyu Zhang, Chunxu Shen, Xiangguo Sun +5
In the digital era, users typically interact with diverse items across multiple domains (e.g., e-commerce, streaming platforms, and social networks), generating intricate heterogen…
Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning
LASA Team, Weiwen Xu, Hou Pong Chan +16
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in understanding common visual elements, largely due to their large-scale datasets and advanced t…
Can Large Language Models Be Query Optimizer for Relational Databases?
Jie Tan, Kangfei Zhao, Rui Li +6
Query optimization, which finds the optimized execution plan for a given query, is a complex planning and decision-making problem within the exponentially growing plan space in dat…
Natural Language-Assisted Multi-modal Medication Recommendation
Jie Tan, Yu Rong, Kangfei Zhao +5
Combinatorial medication recommendation(CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patie…