Inhaltszusammenfassung

The generation of scientific documents is accompanied by decisions of the author, e.g. the type of paper, publishing journal, and selection of an appropriate methodology. This generates a decision tree. Algorithmic support can provide options that the author selects. Combining this approach with Generative Artificial Intelligence (GenAI), that is applied to trees and rules, results in a syntactic and a semantic structure. This conceptual paper discusses the fusion of Retrieval Augmented Gener...The generation of scientific documents is accompanied by decisions of the author, e.g. the type of paper, publishing journal, and selection of an appropriate methodology. This generates a decision tree. Algorithmic support can provide options that the author selects. Combining this approach with Generative Artificial Intelligence (GenAI), that is applied to trees and rules, results in a syntactic and a semantic structure. This conceptual paper discusses the fusion of Retrieval Augmented Generation (RAG), gram-mars, and decision trees with citable tree nodes. Tree nodes are defined by grammar rules with or without decision options and a user driven or randomized selection. The transparency of text generation is supported by a version control for the documentation of the paper evolution.» weiterlesen» einklappen

Autoren

Klassifikation

DFG Fachgebiet:
- ohne Zuordnung

DDC Sachgruppe:
Naturwissenschaften