5 papers
Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling
Zhen Ye, Xu Tan, Aoxiong Yin +8
Joint audio-video generation models have shown that unified generation yields stronger cross-modal coherence than cascaded approaches. However, existing models couple modalities th…
Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking
Hongzhan Lin, Zixin Chen, Zhiqi Shen +5
Large Language Models (LLMs) are increasingly deployed in real-world fact-checking systems, yet existing evaluations focus predominantly on claim verification and overlook the broa…
AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness
Zixin Chen, Hongzhan Lin, Kaixin Li +5
The proliferation of multimodal memes in the social media era demands that multimodal Large Language Models (mLLMs) effectively understand meme harmfulness. Existing benchmarks for…
MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models
Shengkang Wang, Hongzhan Lin, Ziyang Luo +3
Large vision-language models (LVLMs) have significantly improved multimodal reasoning tasks, such as visual question answering and image captioning. These models embed multimodal f…
ScratchEval: Are GPT-4o Smarter than My Child? Evaluating Large Multimodal Models with Visual Programming Challenges
Rao Fu, Ziyang Luo, Hongzhan Lin +2
Recent advancements in large multimodal models (LMMs) have showcased impressive code generation capabilities, primarily evaluated through image-to-code benchmarks. However, these b…