#226 Creating Custom LLMs with Vincent Granville, Founder, CEO & Chief Al Scientist at GenAltechLab.com
DataFramed
Exploring the Creation and Use of Custom Large Language Models
Custom Large Language Models (LLMs) can serve different goals based on their flavor, influenced by factors such as technicality or focus on definitions. One key feature of XLLM is its foundation on taxonomies and knowledge graphs, providing a structured base for information retrieval and augmentation. By leveraging existing structured corpora like Wolfram and Wikipedia, users can enhance taxonomies and knowledge graphs without starting from scratch. These structured resources offer a clear hierarchy of categories and subcategories, aiding in content augmentation and taxonomy enhancement. While tools like Python libraries can assist in retrieving taxonomy elements, a manual effort may be needed to reconstruct the taxonomy. Despite the common misconception of unstructured text, major corpora like Wikipedia and Wolfram possess strong structures that can be recovered and utilized. Leveraging the structured taxonomies of these resources can lead to improved content quality and taxonomy enrichment for custom LLMs, even if external taxonomies are required for specific corporate needs.
Remember Everything You Learn from Podcasts
