LLM Collection

AI and Big Data Common Crawl Datasets Data Poisoning Ego 4D SQuAD The Pile Imbalanced Data

Ablation Active Learning (Machine Learning)Adversarial Machine Learning Affective AI AI Agents AI and Education AI and Finance AI and Medicine AI Assistants AI Ethics AI Generated Music AI Hallucinations AI Hardware AI in Customer Service AI Recommendation Algorithms AI Video Generation AI Voice Transfer Approximate Dynamic Programming Backpropagation Bayesian Machine Learning Binary Classification AI Conversational AI Convolutional Neural Networks Curse of Dimensionality Data Labeling Deep Learning Deep Reinforcement Learning Differential Privacy Dimensionality Reduction Embedding Layer Emergent Behavior Explainable AI F1 Score in Machine Learning F2 Score Feedforward Neural Network Fine Tuning in Deep Learning Gated Recurrent Unit Generative AI Graph Neural Networks Hyperparameter Tuning Intelligent Document Processing Large Language Model (LLM)Loss Function Machine Learning Model Drift Multimodal Learning Natural Language Generation (NLG)Natural Language Processing (NLP)Natural Language Querying (NLQ)Natural Language Understanding (NLU)Precision and Recall Recurrent Neural Networks Transformers Unsupervised Learning Zero-shot Classification Models

Keras Matplotlib Natural Language Toolkit (NLTK)NumPy Pandas PyTorch SciPy Scikit-learn Seaborn Python Package TensorFlow

Acoustic Models Activation Functions AdaGrad AI Alignment Articulatory Synthesis Attention Mechanisms Autoregressive Model Batch Gradient Descent Beam Search Algorithm Benchmarking Candidate Sampling Capsule Neural Network Causal Inference Classification Clustering Algorithms Cognitive Computing Cognitive Map Conditional Variational Autoencoders Concatenative Synthesis Contrastive Learning CURE Algorithm Data Augmentation Domain Adaptation Double Descent End-to-end Learning Evolutionary Algorithms Expectation Maximization Flajolet-Martin Algorithm Forward Propagation Gaussian Processes Generative Adversarial Networks (GANs)Gradient Boosting Machines (GBMs)Gradient Clipping Gradient Scaling Grapheme-to-Phoneme Conversion (G2P)Grounding Hyperparameters Homograph Disambiguation Hooke-Jeeves Algorithm Keyphrase Extraction Knowledge Distillation k-Shingles Latent Dirichlet Allocation (LDA)Markov Decision Process Mixture of Experts Model Interpretability Multimodal AI Neural Radiance Fields Neural Text-to-Speech (NTTS)Online Gradient Descent Out-of-Distribution Detection Overfitting and Underfitting Parametric Neural Networks Prompt Chaining Prompt Engineering Prompt Tuning Regularization Representation Learning Retrieval-Augmented Generation (RAG)RLHF Semi-structured data Sentiment Analysis Sequence Modeling Semantic Kernel Semantic Networks Tokenization Transfer Learning Voice Cloning Winnow Algorithm Word Embeddings

Last updated on January 18, 20241 min read

LLM Collection

The table below includes some of the most influential models, and the order is sorted by their release dates.

Model	Release Date	Developer	License	Description
GPT-4	2023	OpenAI	Custom	Successor to GPT-3, built on a similar architecture but with improvements.
GPT-3	June 2020	OpenAI	Custom	175 billion parameters, known for its versatility and capability.
Turing-NLG	February 2020	Microsoft	Custom	17 billion parameters, aimed at natural language understanding and generation.
GPT-2	February 2019	OpenAI	Modified MIT	Initially withheld from public release due to concerns over potential misuse.
BERT	October 2018	Google	Apache 2.0	Designed to understand the context of words in search queries.
Transformer XL	January 2019	Google/CMU	Apache 2.0	Extended Transformer model to handle longer sequences of text.
GPT	June 2018	OpenAI	Modified MIT	First Generative Pre-trained Transformer with 117M parameters.
ELMo	March 2018	Allen Institute	Apache 2.0	Deep contextualized word representations, allowing for rich word meanings.

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