KnowSA_CKP uses comparative knowledge probing to selectively augment LLM prompts for items with knowledge gaps, improving recommendation accuracy and context efficiency.
arXiv preprint arXiv:2403.09162 , year=
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Dynamic model of data-driven automation with heterogeneous accumulating data and spillovers derives conditions for partial versus full automation, shows asymptotic power-law decay in labor share, generic inefficiency, and with endogenous capital, explosive growth but stagnant long-run wages.
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Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders
KnowSA_CKP uses comparative knowledge probing to selectively augment LLM prompts for items with knowledge gaps, improving recommendation accuracy and context efficiency.
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Data-Driven Automation
Dynamic model of data-driven automation with heterogeneous accumulating data and spillovers derives conditions for partial versus full automation, shows asymptotic power-law decay in labor share, generic inefficiency, and with endogenous capital, explosive growth but stagnant long-run wages.