4 citations · 10 across the 6 of their papers we have counts for
6 papers
Safety in Large Reasoning Models: A Survey
Cheng Wang, Yue Liu, Baolong Bi +9
Large Reasoning Models (LRMs) have exhibited extraordinary prowess in tasks like mathematics and coding, leveraging their advanced reasoning capabilities. Nevertheless, as these ca…
Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models
Jiaying Wu, Fanxiao Li, Zihang Fu +2
The impact of multimodal misinformation arises not only from factual inaccuracies but also from the misleading narratives that creators deliberately embed. Interpreting such creato…
GuardReasoner: Towards Reasoning-based LLM Safeguards
Yue Liu, Hongcheng Gao, Shengfang Zhai +9
As LLMs increasingly impact safety-critical applications, ensuring their safety using guardrails remains a key challenge. This paper proposes GuardReasoner, a new safeguard for LLM…
Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training
Yufei He, Zhenyu Hou, Yukuo Cen +5
Graph pre-training has been concentrated on graph-level tasks involving small graphs (e.g., molecular graphs) or learning node representations on a fixed graph. Extending graph pre…
Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding
Ailin Deng, Zhirui Chen, Bryan Hooi
Large Vision-Language Models (LVLMs) are susceptible to object hallucinations, an issue in which their generated text contains non-existent objects, greatly limiting their reliabil…
Information Extraction in Low-Resource Scenarios: Survey and Perspective
Shumin Deng, Yubo Ma, Ningyu Zhang +2
Information Extraction (IE) seeks to derive structured information from unstructured texts, often facing challenges in low-resource scenarios due to data scarcity and unseen classe…