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整合预训练语言模型知识的泊松过程主题模型AI 解读

Topic modeling is traditionally applied to word counts without accounting for the context in which words appear. Recent advances in large language models (LLMs) offer contextualized word embeddings that capture deeper semantic meaning and relationships. We aim to leverage such embeddings to improve topic modeling.We use a pre-trained LLM to convert each document into a sequence of word embeddings. This sequence is then modeled as a Poisson point process, with its intensity…

整群再随机化的设计型理论AI 解读

Summary Complete randomization balances covariates on average, but covariate imbalance often exists in finite samples. Rerandomization can ensure covariate balance in the realized experiment by discarding the undesired treatment assignments. Many field experiments in public health and social sciences assign the treatment at the cluster level due to logistical constraints or policy considerations. Moreover, they are frequently combined with re-randomization in the design…