5 accepted papers
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating up-to-date external knowledge, yet real-world web environments present unique challenges. These limitations manifest as two key challenges: pervasive misinformation in the web environment, which introduces unre
Large language model (LLM)–based agents are increasingly trained with reinforcement learning (RL) to enhance their ability to interact with external environments through tool use, particularly in search-based settings that require multi-turn reasoning and knowledge acquisition. However, existing app…
The financial industry faces a substantial workload in verifying document images. Existing methods based on visual features struggle to identify fraudulent document images due to the lack of visual clues on the tampering region. This paper proposes CSIAD (Cross-Sample Image Anomaly Detection) by lev…
Modeling social media users is the core of social governance in the digital society. Existing works have incorporated different digital traces to better learn the representations of social media users, including text information encoded by pre-trained language models and social network information e…
Based on the significant improvement of model robustness by AT (Adversarial Training), various variants have been proposed to further boost the performance. Well-recognized methods have focused on different components of AT (e.g., designing loss functions and leveraging additional unlabeled data). I…