A user on r/MachineLearning is seeking the theoretical basis for using the consensus of large language models (LLMs) as a probability estimator for real-world events. The user questions whether model errors are sufficiently uncorrelated, given shared training data and architectures, and if this consensus approach might lead to false confidence due to common blind spots. Additionally, the user is interested in how LLM ensembles handle novel events that fall outside their training distribution. AI
IMPACT Raises questions about the reliability and theoretical grounding of using LLM ensembles for probabilistic forecasting.
RANK_REASON The cluster contains a user question about the theoretical underpinnings of a technique, rather than a new release or development.
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