Introduces diversity of extensions in argumentation frameworks via symmetric difference and gives a systematic complexity classification for deciding existence of k-diverse extensions and computing maximum diversity.
Argumentation in Artificial Intelligence , volume =
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Instructions trigger a production-centered mechanism in language models, with task-specific information stable in input tokens but varying strongly in output tokens and correlating with behavior.
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Diversity of Extensions in Abstract Argumentation
Introduces diversity of extensions in argumentation frameworks via symmetric difference and gives a systematic complexity classification for deciding existence of k-diverse extensions and computing maximum diversity.
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Instructions Shape Production of Language, not Processing
Instructions trigger a production-centered mechanism in language models, with task-specific information stable in input tokens but varying strongly in output tokens and correlating with behavior.