1 citations · 1 across the 16 of their papers we have counts for
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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…
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Multimodal large language models (MLLMs) are prone to non-factual or outdated knowledge issues, which can manifest as misreading and misrecognition errors due to the complexity of…
Automated Similarity Metric Generation for Recommendation
Liang Qu, Yun Lin, Wei Yuan +3
The embedding-based architecture has become the dominant approach in modern recommender systems, mapping users and items into a compact vector space. It then employs predefined sim…
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…