concept

Transformer models

Also known as: Transformer model, Transformer-based models

Facts (12)

Sources

A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv Mar 12, 2026 4 facts

referenceYun et al. (2019) demonstrated that for any sequence-to-sequence function, there exists a Transformer model that can approximate it, provided the number of layers scales exponentially in model dimension or input sequence length, while the size of each layer remains independent of the dimension and length.

claimThe paper 'Separations in the representational capabilities of transformers and recurrent architectures' demonstrates that there are distinct differences in the representational power between transformer-based models and recurrent neural network architectures.

claimTransformer-based models in NLP tasks commonly utilize the Adam optimizer and its variants, as documented in research by Vaswani et al. (2017b), Radford et al. (2019), and Brown et al. (2020).

claimVon Oswald et al. (2023b) proposed a constructive approach under the auto-regressive setting that reaches conclusions similar to those regarding online gradient descent in Transformer models.

A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer Nov 4, 2024 3 facts

referenceWang et al. (2023) published 'Financial fraud detection based on deep learning: Towards large-scale pre-training transformer models' in the China Conference on Knowledge Graph and Semantic Computing.

claimTransformer models utilize a self-attention mechanism to process text more efficiently and accurately.

claimVaswani et al. introduced transformer models in 2017, which serve as the foundation for modern LLMs such as BERT and GPT.

LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org arXiv Oct 23, 2025 1 fact

claimThe adoption of deep learning architectures, such as BiLSTM-CRF and Transformer-based models, marked a paradigm shift toward data-driven feature learning in Knowledge Extraction, as discussed by Yang et al. (2022b).

Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org arXiv Nov 7, 2024 1 fact

referenceHu et al. (2022a) developed a neural-symbolic edit grammar designed to fix bugs within transformer models.

Real-Time Evaluation Models for RAG: Who Detects Hallucinations ... cleanlab.ai Cleanlab Apr 7, 2025 1 fact

referenceThe Hughes Hallucination Evaluation Model (HHEM) is a Transformer model trained by Vectara to distinguish between hallucinated and correct responses from various Large Language Models across different context and response data.

A Comprehensive Benchmark and Evaluation Framework for Multi ... arxiv.org arXiv Jan 6, 2026 1 fact

referenceClinical-Longformer and Clinical-BERT are transformer models designed for processing long clinical documents, as described in the Journal of Biomedical Informatics in 2022.

Neuro-Symbolic AI: Explainability, Challenges & Future Trends linkedin.com Ali Rouhanifar · LinkedIn Dec 15, 2025 1 fact

claimGenerative AI models, including Large Language Models (LLMs), Generative Adversarial Networks (GANs), and Transformer models, function by training neural networks on vast datasets to learn underlying patterns, which enables the generation of new outputs.