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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ANCHOR constructs dense hierarchical factor spaces via LLM generation and clustering, then augments Naive Bayes with a causal Bayesian network to reduce unknown predictions and improve reliability of LLM-based probability estimates.
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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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ANCHOR: Abductive Network Construction with Hierarchical Orchestration for Reliable Probability Inference in Large Language Models
ANCHOR constructs dense hierarchical factor spaces via LLM generation and clustering, then augments Naive Bayes with a causal Bayesian network to reduce unknown predictions and improve reliability of LLM-based probability estimates.