5 papers
Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings
Aakriti Agrawal, Gouthaman KV, Rohith Aralikatti +6
Hallucinations in Large Vision-Language Models (LVLMs) remain a persistent challenge, often stemming from inadequate integration of visual information during multimodal reasoning.…
Large Language Models and Causal Inference in Collaboration: A Survey
Xiaoyu Liu, Paiheng Xu, Junda Wu +10
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal…
Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine
Yifan Yang, Qiao Jin, Robert Leaman +15
The remarkable capabilities of Large Language Models (LLMs) make them increasingly compelling for adoption in real-world healthcare applications. However, the risks associated with…
FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model
Zezheng Song, Jiaxin Yuan, Haizhao Yang
The fast simulation of dynamical systems is a key challenge in many scientific and engineering applications, such as weather forecasting, disease control, and drug discovery. With…
CSRec: Rethinking Sequential Recommendation from A Causal Perspective
Xiaoyu Liu, Jiaxin Yuan, Yuhang Zhou +3
The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions. Most existing approaches frame the task as sequential prediction based on use…