6 citations · 8 across the 5 of their papers we have counts for
5 papers
OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models
Yue Ding, Yiyan Ji, Jungang Li +12
Omni-modal Large Language Models (Omni-LLMs) have demonstrated strong capabilities in audio-video understanding tasks. However, their reliance on long multimodal token sequences le…
MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?
Yi-Fan Zhang, Huanyu Zhang, Haochen Tian +10
Comprehensive evaluation of Multimodal Large Language Models (MLLMs) has recently garnered widespread attention in the research community. However, we observe that existing benchma…
Can Large Language Models Detect Rumors on Social Media?
Qiang Liu, Xiang Tao, Junfei Wu +2
In this work, we investigate to use Large Language Models (LLMs) for rumor detection on social media. However, it is challenging for LLMs to reason over the entire propagation info…
Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
Junfei Wu, Qiang Liu, Ding Wang +4
Object hallucination has been an Achilles' heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that t…
Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasing
Qiang Liu, Junfei Wu, Shu Wu +1
Evidence-aware fake news detection aims to conduct reasoning between news and evidence, which is retrieved based on news content, to find uniformity or inconsistency. However, we f…