Malleable Prompting reifies subjective preferences from natural language into GUI widgets and modulates LLM token probabilities during decoding to enable controllable generation, with a user study showing improved precision and perceived controllability over standard prompting.
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Applies quantum theory to model incompatibility and interference between relevance dimensions via a cognitive analogue of a quantum physics experiment in a user study.
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From Words to Widgets for Controllable LLM Generation
Malleable Prompting reifies subjective preferences from natural language into GUI widgets and modulates LLM token probabilities during decoding to enable controllable generation, with a user study showing improved precision and perceived controllability over standard prompting.
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Modelling Dynamic Interactions between Relevance Dimensions
Applies quantum theory to model incompatibility and interference between relevance dimensions via a cognitive analogue of a quantum physics experiment in a user study.