PluRule is a new multimodal multilingual benchmark showing that state-of-the-art vision-language models perform only marginally better than a trivial baseline at detecting specific rule violations in pluralistic online communities.
arXiv preprint arXiv:2307.12980 , year=
7 Pith papers cite this work, alongside 63 external citations. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 7representative citing papers
ZEBRA reduces the base-to-novel generalization gap in audio-language models by fusing zero-shot and prompt-learning logits with entropy regularization.
EPIG is a training-free prompt enrichment technique using valence-arousal representations that reduces mean arousal error by 14% versus naive insertion and 12% versus LLM expansion on a 10-prompt benchmark while preserving valence and semantic consistency.
Optimized prompts for vision foundation models improve cowpea detection accuracy by over 0.35 mAP on synthetic data and transfer effectively to real fields without manual annotations.
Zero-shot VLMs reach at most 62% accuracy on agricultural classification tasks while supervised models like YOLO11 perform markedly higher, indicating they are not ready to replace task-specific systems.
A survey of personalization techniques and foundation model adaptations in federated settings for privacy-preserving recommendations, emphasizing their architectural intersection.
A tutorial synthesizing foundations, recent models such as PALO and Maya, and low-cost methods for tri-modal multilingual AI in resource-constrained settings.
citing papers explorer
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PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media
PluRule is a new multimodal multilingual benchmark showing that state-of-the-art vision-language models perform only marginally better than a trivial baseline at detecting specific rule violations in pluralistic online communities.
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ZEBRA: Zero-Shot Entropy-Regularized Prompt Learning for Base-to-Novel Generalization in Audio-Language Models
ZEBRA reduces the base-to-novel generalization gap in audio-language models by fusing zero-shot and prompt-learning logits with entropy regularization.
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EPIG: Emotion-Based Prompting for Personalised Image Generation
EPIG is a training-free prompt enrichment technique using valence-arousal representations that reduces mean arousal error by 14% versus naive insertion and 12% versus LLM expansion on a 10-prompt benchmark while preserving valence and semantic consistency.
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Does Your VFM Speak Plant? The Botanical Grammar of Vision Foundation Models for Object Detection
Optimized prompts for vision foundation models improve cowpea detection accuracy by over 0.35 mAP on synthetic data and transfer effectively to real fields without manual annotations.
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Are vision-language models ready to zero-shot replace supervised classification models in agriculture?
Zero-shot VLMs reach at most 62% accuracy on agricultural classification tasks while supervised models like YOLO11 perform markedly higher, indicating they are not ready to replace task-specific systems.
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A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
A survey of personalization techniques and foundation model adaptations in federated settings for privacy-preserving recommendations, emphasizing their architectural intersection.
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Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages
A tutorial synthesizing foundations, recent models such as PALO and Maya, and low-cost methods for tri-modal multilingual AI in resource-constrained settings.