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CERN for AGI: A Theoretical Framework for Autonomous Simulation-Based Artificial Intelligence Testing and Alignment
This paper explores the potential of a multidisciplinary approach to testing and aligning artificial general intelligence (AGI) and LLMs.
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces
Despite the prevalence of tabular datasets, few-shot learning remains under-explored within this domain.
Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models
Drawing upon the reasoning capabilities of LLMs, TEMED-LLM goes beyond traditional extraction techniques, accurately inferring tabular features, even when their names are not explicitly mentioned in the text.
Gaussian Conditional Random Fields for Classification
The extended method of local variational approximation of sigmoid function is used for solving empirical Bayes in GCRFBCb variant, whereas MAP value of latent variables is the basis for learning and inference in the GCRFBCnb variant.
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