1 citations · 1 across the 4 of their papers we have counts for
5 papers
Not All Answers Are Contextually Persuadable: Inference Dynamics in Large Language Models under Contextual Influence
Zongye Hu, Weiqing Luo, Yanjie Fu +3
At the core of modern prompting techniques is contextual sensitivity, the ability of large language models to adapt their predictions based on inference-time context. Despite its c…
Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation
Weiqing Luo, Zongye Hu, Xiao Wang +3
Visual evidence selection is a critical component of multimodal retrieval-augmented generation (RAG), yet existing methods typically rely on semantic relevance or surface-level sim…
Task-Aware Resolution Optimization for Visual Large Language Models
Weiqing Luo, Zhen Tan, Yifan Li +4
Real-world vision-language applications demand varying levels of perceptual granularity. However, most existing visual large language models (VLLMs), such as LLaVA, pre-assume a fi…
Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph
Qiaosheng Chen, Kaijia Huang, Xiao Zhou +3
The rapid growth of open source machine learning (ML) resources, such as models and datasets, has accelerated IR research. However, existing platforms like Hugging Face do not expl…
TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance
Weiqing Luo, Chonggang Song, Lingling Yi +1
Combining semantic information with behavioral data is a crucial research area in recommender systems. A promising approach involves leveraging external knowledge to enrich behavio…