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SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model

Autor: Huang, Zhenglin, Hu, Jinwei, Li, Xiangtai, He, Yiwei, Zhao, Xingyu, Peng, Bei, Wu, Baoyuan, Huang, Xiaowei, Cheng, Guangliang

The rapid advancement of generative models in creating highly realistic images poses substantial risks for misinformation dissemination. For instance, a synthetic image, when shared on social media, can mislead extensive audiences and erode trust in

Externí odkaz: http://arxiv.org/abs/2412.04292

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Adaptive Guardrails For Large Language Models via Trust Modeling and In-Context Learning

Autor: Hu, Jinwei, Dong, Yi, Huang, Xiaowei

Guardrails have become an integral part of Large language models (LLMs), by moderating harmful or toxic response in order to maintain LLMs' alignment to human expectations. However, the existing guardrail methods do not consider different needs and a

Externí odkaz: http://arxiv.org/abs/2408.08959

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A Combination Model for Time Series Prediction using LSTM via Extracting Dynamic Features Based on Spatial Smoothing and Sequential General Variational Mode Decomposition

Autor: Liu, Jianyu, Chen, Wei, Zhang, Yong, Chen, Zhenfeng, Wan, Bin, Hu, Jinwei

In order to solve the problems such as difficult to extract effective features and low accuracy of sales volume prediction caused by complex relationships such as market sales volume in time series prediction, we proposed a time series prediction met

Externí odkaz: http://arxiv.org/abs/2406.03144

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Safeguarding Large Language Models: A Survey

Autor: Dong, Yi, Mu, Ronghui, Zhang, Yanghao, Sun, Siqi, Zhang, Tianle, Wu, Changshun, Jin, Gaojie, Qi, Yi, Hu, Jinwei, Meng, Jie, Bensalem, Saddek, Huang, Xiaowei

In the burgeoning field of Large Language Models (LLMs), developing a robust safety mechanism, colloquially known as "safeguards" or "guardrails", has become imperative to ensure the ethical use of LLMs within prescribed boundaries. This article prov

Externí odkaz: http://arxiv.org/abs/2406.02622

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Explainable Global Wildfire Prediction Models using Graph Neural Networks

Autor: Chen, Dayou, Cheng, Sibo, Hu, Jinwei, Kasoar, Matthew, Arcucci, Rossella

Wildfire prediction has become increasingly crucial due to the escalating impacts of climate change. Traditional CNN-based wildfire prediction models struggle with handling missing oceanic data and addressing the long-range dependencies across distan

Externí odkaz: http://arxiv.org/abs/2402.07152

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Building Guardrails for Large Language Models

Autor: Dong, Yi, Mu, Ronghui, Jin, Gaojie, Qi, Yi, Hu, Jinwei, Zhao, Xingyu, Meng, Jie, Ruan, Wenjie, Huang, Xiaowei

As Large Language Models (LLMs) become more integrated into our daily lives, it is crucial to identify and mitigate their risks, especially when the risks can have profound impacts on human users and societies. Guardrails, which filter the inputs or

Externí odkaz: http://arxiv.org/abs/2402.01822

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