Knowledge Tree
Relations (1)
related 2.00 — strongly supporting 3 facts
GPT is explicitly categorized as a type of Large Language Model in [1], and [2] further identifies GPTs as specific examples of this class of technology. Additionally, [3] contrasts the performance of domain-specific models against the general characteristics of GPT-based Large Language Models.
Facts (3)
Sources
Neurosymbolic AI: The Future of AI After LLMs - LinkedIn linkedin.com 1 fact
claimGraphMERT adheres to the strict rules of a professional-grade ontology, allowing it to provide breakthrough ideas from domain-specific data rather than the surface-level word correlations and hallucinations associated with GPT-based LLMs.
Combining large language models with enterprise knowledge graphs frontiersin.org 1 fact
claimPrompting with large Large Language Models (like GPTs) can underperform in Named Entity Recognition compared to fine-tuned smaller Pre-trained Language Models (like BERT derivations), especially when more training data is available (Gutierrez et al., 2022; Keloth et al., 2024; Pecher et al., 2024; Törnberg, 2024).
Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org 1 fact
claimExamples of large language models include Google’s BERT, Google's T5, and OpenAI’s GPT series.