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cs.CV2025
Curing Semantic Drift: A Dynamic Approach to Grounding Generation in Large Vision-Language Models
Jiahe Chen, Jiaying He, Qiyuan Chen +6
Large Vision-Language Models (LVLMs) face a tug-of-war between powerful linguistic priors and visual evidence, often leading to \emph{semantic drift}: a progressive detachment from…
cs.CL2025
From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMs
Guocong Li, Weize Liu, Yihang Wu +4
Large language models (LLMs) exhibit excellent performance in natural language processing (NLP), but remain highly sensitive to the quality of input queries, especially when these…
cs.LG2025
MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trials
Qian Shao, Bang Du, Zepeng Li +6
High failure rates in cardiac drug development necessitate virtual clinical trials via electrocardiogram (ECG) generation to reduce risks and costs. However, existing ECG generatio…