1 citations · 1 across the 10 of their papers we have counts for
11 papers
DMC-CF: Dynamic Multimodal CounterFactual QA benchmark for Causal Reasoning
Junzhe Zhang, Huixuan Zhang, Guirong Wang +5
With the rapid advancement of multimodal large language models (MLLMs), models have demonstrated increasingly powerful multimodal capabilities. However, whether MLLMs trained throu…
KBE-DME: Dynamic Multimodal Evaluation via Knowledge Enhanced Benchmark Evolution
Junzhe Zhang, Huixuan Zhang, Xiaojun Wan
The rapid progress of multimodal large language models (MLLMs) calls for more reliable evaluation protocols. Existing static benchmarks suffer from the potential risk of data conta…
Exploring Causal Effect of Social Bias on Faithfulness Hallucinations in Large Language Models
Zhenliang Zhang, Junzhe Zhang, Xinyu Hu +2
Large language models (LLMs) have achieved remarkable success in various tasks, yet they remain vulnerable to faithfulness hallucinations, where the output does not align with the…
ICR Probe: Tracking Hidden State Dynamics for Reliable Hallucination Detection in LLMs
Zhenliang Zhang, Xinyu Hu, Huixuan Zhang +2
Large language models (LLMs) excel at various natural language processing tasks, but their tendency to generate hallucinations undermines their reliability. Existing hallucination…
How Much To Guide: Revisiting Adaptive Guidance in Classifier-Free Guidance Text-to-Vision Diffusion Models
Huixuan Zhang, Junzhe Zhang, Xiaojun Wan
With the rapid development of text-to-vision generation diffusion models, classifier-free guidance has emerged as the most prevalent method for conditioning. However, this approach…
MINOS: A Multimodal Evaluation Model for Bidirectional Generation Between Image and Text
Junzhe Zhang, Huixuan Zhang, Xinyu Hu +4
Evaluation is important for multimodal generation tasks, while traditional multimodal evaluation metrics suffer from several limitations. With the rapid progress of MLLMs, there is…
C-FAITH: A Chinese Fine-Grained Benchmark for Automated Hallucination Evaluation
Xu Zhang, Zhifei Liu, Jiahao Wang +4
Despite the rapid advancement of large language models, they remain highly susceptible to generating hallucinations, which significantly hinders their widespread application. Hallu…
Exploring and Evaluating Multimodal Knowledge Reasoning Consistency of Multimodal Large Language Models
Boyu Jia, Junzhe Zhang, Huixuan Zhang +1
In recent years, multimodal large language models (MLLMs) have achieved significant breakthroughs, enhancing understanding across text and vision. However, current MLLMs still face…
MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency
Junzhe Zhang, Huixuan Zhang, Xunjian Yin +4
Multimodal large language models (MLLMs) are prone to non-factual or outdated knowledge issues, which can manifest as misreading and misrecognition errors due to the complexity of…
Quantity Matters: Towards Assessing and Mitigating Number Hallucination in Large Vision-Language Models
Huixuan Zhang, Junzhe Zhang, Xiaojun Wan
Large-scale vision-language models have demonstrated impressive skill in handling tasks that involve both areas. Nevertheless, these models frequently experience significant issues…
EAMA : Entity-Aware Multimodal Alignment Based Approach for News Image Captioning
Junzhe Zhang, Huixuan Zhang, Xunjian Yin +1
News image captioning requires model to generate an informative caption rich in entities, with the news image and the associated news article. Current MLLMs still bear limitations…