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.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
representative citing papers
A scalable Aumann-Shapley attribution method for million-agent systems reveals that small-scale samples structurally misattribute emergence under nonlinear macro indicators, as shown by the Attribution Scaling Bias theorem.
Guardrail sampling strategies embedded in line charts increase user trust, improve accuracy of performance judgments, and raise perceived completeness of context in persuasive visualizations for COVID-19 and stock data.
Foundation models are large adaptable AI systems with emergent capabilities that offer broad opportunities but carry risks from homogenization, opacity, and inherited defects across downstream applications.
Survival analysis of three years of X posts shows conspiracy claims with greater semantic mutations have substantially longer lifespans, linked to changes in pronouns, social words, cognitive terms, and actor-action-target structures.
Simulations show information overload decreases source localization effectiveness in networks, with Erdős-Rényi graphs more resilient than Barabási-Albert ones and a reversal where less dense networks perform better under strong overload.
citing papers explorer
-
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.
-
Attributing Emergence in Million-Agent Systems
A scalable Aumann-Shapley attribution method for million-agent systems reveals that small-scale samples structurally misattribute emergence under nonlinear macro indicators, as shown by the Attribution Scaling Bias theorem.
-
Guardrail Selection in Line Charts to Contextualize Persuasive Visualizations
Guardrail sampling strategies embedded in line charts increase user trust, improve accuracy of performance judgments, and raise perceived completeness of context in persuasive visualizations for COVID-19 and stock data.
-
On the Opportunities and Risks of Foundation Models
Foundation models are large adaptable AI systems with emergent capabilities that offer broad opportunities but carry risks from homogenization, opacity, and inherited defects across downstream applications.
-
Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media
Survival analysis of three years of X posts shows conspiracy claims with greater semantic mutations have substantially longer lifespans, linked to changes in pronouns, social words, cognitive terms, and actor-action-target structures.
-
Nonlinear dynamics of information overload: Impact on source localization in complex networks
Simulations show information overload decreases source localization effectiveness in networks, with Erdős-Rényi graphs more resilient than Barabási-Albert ones and a reversal where less dense networks perform better under strong overload.