LLM Collection
Acoustic ModelsActivation FunctionsAdaGradAI AlignmentAI Emotion RecognitionAI GuardrailsAI Speech EnhancementArticulatory SynthesisAttention MechanismsAuto ClassificationAutoregressive ModelBatch Gradient DescentBeam Search AlgorithmBenchmarkingCandidate SamplingCapsule Neural NetworkCausal InferenceClassificationClustering AlgorithmsCognitive ComputingCognitive MapComputational CreativityComputational LinguisticsComputational PhenotypingComputational SemanticsConditional Variational AutoencodersConcatenative SynthesisContext-Aware ComputingContrastive LearningCURE AlgorithmData AugmentationData DriftDeepfake DetectionDiffusionDomain AdaptationDouble DescentEnd-to-end LearningEvolutionary AlgorithmsExpectation MaximizationFeature Store for Machine LearningFederated LearningFew Shot LearningFlajolet-Martin AlgorithmForward PropagationGaussian ProcessesGenerative Adversarial Networks (GANs)Gradient Boosting Machines (GBMs)Gradient ClippingGradient ScalingGrapheme-to-Phoneme Conversion (G2P)GroundingHuman-in-the-Loop AIHyperparametersHomograph DisambiguationHooke-Jeeves AlgorithmIncremental LearningInstruction TuningKeyphrase ExtractionKnowledge DistillationKnowledge Representation and Reasoningk-ShinglesLatent Dirichlet Allocation (LDA)Markov Decision ProcessMetaheuristic AlgorithmsMixture of ExpertsModel InterpretabilityMultimodal AIMultitask Prompt TuningNamed Entity RecognitionNeural Radiance FieldsNeural Style TransferNeural Text-to-Speech (NTTS)One-Shot LearningOnline Gradient DescentOut-of-Distribution DetectionOverfitting and UnderfittingParametric Neural Networks Part-of-Speech TaggingPrompt ChainingPrompt EngineeringPrompt TuningQuantum Machine Learning AlgorithmsRandom ForestRegularizationRepresentation LearningRetrieval-Augmented Generation (RAG)RLHFSemantic Search AlgorithmsSemi-structured dataSentiment AnalysisSequence ModelingSemantic KernelSemantic NetworksSpike Neural NetworksStatistical Relational LearningSymbolic AITokenizationTransfer LearningVoice CloningWinnow AlgorithmWord 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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