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UniAIDet: A Unified and Universal Benchmark for AI-Generated Image Content Detection and Localization
Huixuan Zhang, Xiaojun Wan
With the rapid proliferation of image generative models, the authenticity of digital images has become a significant concern. While existing studies have proposed various methods f…
MT2IBench: A Large-Scale Multi-Category, Multi-Instance, Multi-Relation Text-to-Image Benchmark
Huixuan Zhang, Xiaojun Wan
Text-to-image models are known to struggle with generating images that perfectly align with textual prompts. Several previous studies have focused on evaluating image-text alignmen…
HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy
Fan Xu, Xinyu Hu, Zhenghan Yu +6
The increasing reliance on natural language generation (NLG) models, particularly large language models, has raised concerns about the reliability and accuracy of their outputs. A…
JointCQ: Improving Factual Hallucination Detection with Joint Claim and Query Generation
Fan Xu, Huixuan Zhang, Zhenliang Zhang +2
Current large language models (LLMs) often suffer from hallucination issues, i,e, generating content that appears factual but is actually unreliable. A typical hallucination detect…
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…
Re-Thinking the Automatic Evaluation of Image-Text Alignment in Text-to-Image Models
Huixuan Zhang, Xiaojun Wan
Text-to-image models often struggle to generate images that precisely match textual prompts. Prior research has extensively studied the evaluation of image-text alignment in text-t…
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…