A computational argumentation framework evaluates LLM summaries of parliamentary debates by checking preservation of formal argument structures tied to contested proposals.
Eagan and Winston Maxwell , title =
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Generative LMs in laissez-faire open-ended prompting settings disproportionately generate subordinated portrayals of minoritized race, gender, and sexual orientation identities at rates hundreds to thousands of times higher than empowering ones.
EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.
citing papers explorer
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Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation
A computational argumentation framework evaluates LLM summaries of parliamentary debates by checking preservation of formal argument structures tied to contested proposals.
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Laissez-Faire Harms: Algorithmic Biases in Generative Language Models
Generative LMs in laissez-faire open-ended prompting settings disproportionately generate subordinated portrayals of minoritized race, gender, and sexual orientation identities at rates hundreds to thousands of times higher than empowering ones.
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AI at the Front Lines of Platform Governance: Using LLMs to Support Illegal Content Reporting under the Digital Services Act
EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.