5 papers
Transformers are Bayesian Networks
Gregory Coppola
Transformers are the dominant architecture in AI, yet why they work remains poorly understood. This paper offers a precise answer: a transformer is a Bayesian network. We establish…
Statistical Parsing for Logical Information Retrieval
Greg Coppola
In previous work (Coppola, 2024) we introduced the Quantified Boolean Bayesian Network (QBBN), a logical graphical model that implements the forward fragment of natural deduction (…
Logical Discrete Graphical Models Must Supplement Large Language Models for Information Synthesis
Gregory Coppola
Given the emergent reasoning abilities of large language models, information retrieval is becoming more complex. Rather than just retrieve a document, modern information retrieval…
A Categorization of Complexity Classes for Information Retrieval and Synthesis Using Natural Logic
Gregory Coppola
Given the emergent reasoning abilities of large language models, information retrieval is becoming more complex. Rather than just retrieve a document, modern information retrieval…
The Quantified Boolean Bayesian Network: Theory and Experiments with a Logical Graphical Model
Gregory Coppola
This paper introduces the Quantified Boolean Bayesian Network (QBBN), which provides a unified view of logical and probabilistic reasoning. The QBBN is meant to address a central p…