A factorized generative Markov model is proposed for distributed computing systems to enable tractable simulation, inference, and policy learning, shown in a collaborative AI inference case study.
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LLMs achieve Pearson correlations up to 0.97 and 94% classification accuracy on product desirability sentiment from qualitative data, outperforming lexicon and transformer baselines while providing confidence ratings and rationales.
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Brief Announcement: Generative Markov Model for Distributed Computing Systems
A factorized generative Markov model is proposed for distributed computing systems to enable tractable simulation, inference, and policy learning, shown in a collaborative AI inference case study.