concept

GPT

Also known as: GPT series, GPTs, Generative Pre-trained Transformer

Facts (13)

Sources

Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org arXiv Jul 9, 2024 2 facts

claimThe first Generative Pre-trained Transformer (GPT) focused on generating text by predicting the next word.

claimExamples of large language models include Google’s BERT, Google's T5, and OpenAI’s GPT series.

Medical Hallucination in Foundation Models and Their ... medrxiv.org medRxiv Mar 3, 2025 1 fact

claimProminent large language models include OpenAI’s GPT series, Google’s Gemini, Anthropic’s Claude, and Meta’s Llama family.

The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org arXiv Jul 11, 2024 1 fact

referenceTransformer-based pre-trained language models are categorized into encoder-only models (e.g., BERT) for understanding and classifying text, decoder-only models (e.g., GPT) for generating coherent text, and encoder-decoder models (e.g., T5) for tasks requiring both comprehension and generation.

What is Open Source Software? - HotWax Systems hotwaxsystems.com HotWax Systems Aug 11, 2025 1 fact

claimMistral, Gemma, Falcon, and Command R/R+ serve as open alternatives to commercial APIs such as OpenAI’s GPT and Anthropic’s Claude.

The Impact of Open Source on Digital Innovation linkedin.com LinkedIn 1 fact

accountTechChange attempted to self-host the LLaMA open source model but eventually pivoted back to proprietary tools like GPT and Claude due to requirements for speed, support, and access to a more robust ecosystem.

The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org arXiv 1 fact

claimContemporary research in neuro-symbolic AI and large-scale pre-trained models, such as BERT, GPT, and hybrid reinforcement learning models, exemplifies the convergence of connectionist and symbolic paradigms.

Neurosymbolic AI: The Future of AI After LLMs - LinkedIn linkedin.com Charley Miller · LinkedIn Nov 11, 2025 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 Frontiers Aug 26, 2024 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).

Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org arXiv Feb 16, 2025 1 fact

referenceThe sequential paradigm in Neuro-Symbolic AI (NSAI) architectures relies on neural encodings of symbolic data, such as text or structured information, to perform complex transformations before outputting results in symbolic form; this includes techniques like Retrieval-Augmented Generation (RAG), GraphRAG, and Seq2Seq models like GPT.

The construction and refined extraction techniques of knowledge ... nature.com Nature Feb 10, 2026 1 fact

claimLLaMA3 70B is a third-generation language model with 70 billion parameters that employs a different architecture and pre-training strategy than the GPT series.

The Evidence for AI Consciousness, Today - AI Frontiers ai-frontiers.org AI Frontiers Dec 8, 2025 1 fact

procedureResearchers tested GPT, Claude, and Gemini AI models by prompting them to engage in sustained recursive attention—specifically instructing them to focus on their own focus and feed output back into input—while avoiding leading language about consciousness. This testing method resulted in virtually all trials producing consistent reports of inner experiences, whereas control conditions that included priming the models with consciousness ideation produced essentially no such reports.

Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org Frontiers 1 fact

referenceDecoder-only models, such as GPT, OPT, and LLaMA, utilize unidirectional attention and auto-regressive token prediction to excel in text generation tasks like chatbots, text summarization, and code generation.