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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…
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…
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…
PaCoST: Paired Confidence Significance Testing for Benchmark Contamination Detection in Large Language Models
Huixuan Zhang, Yun Lin, Xiaojun Wan
Large language models (LLMs) are known to be trained on vast amounts of data, which may unintentionally or intentionally include data from commonly used benchmarks. This inclusion…
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…