Listado de LLMs

Listado de LLMs


Esta sección está en pleno desarrollo.

Esta sección consta de una colección y resumen de LLMs notables y fundamentales. (Datos adoptados de Papers with Code (opens in a new tab) y el trabajo reciente de Zhao et al. (2023) (opens in a new tab).


ModelRelease DateDescription
BERT (opens in a new tab)2018Bidirectional Encoder Representations from Transformers
GPT (opens in a new tab)2018Improving Language Understanding by Generative Pre-Training
RoBERTa (opens in a new tab)2019A Robustly Optimized BERT Pretraining Approach
GPT-2 (opens in a new tab)2019Language Models are Unsupervised Multitask Learners
T5 (opens in a new tab)2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
BART (opens in a new tab)2019Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
ALBERT (opens in a new tab)2019A Lite BERT for Self-supervised Learning of Language Representations
XLNet (opens in a new tab)2019Generalized Autoregressive Pretraining for Language Understanding and Generation
CTRL (opens in a new tab)2019CTRL: A Conditional Transformer Language Model for Controllable Generation
ERNIE (opens in a new tab)2019ERNIE: Enhanced Representation through Knowledge Integration
GShard (opens in a new tab)2020GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
GPT-3 (opens in a new tab)2020Language Models are Few-Shot Learners
LaMDA (opens in a new tab)2021LaMDA: Language Models for Dialog Applications
PanGu-α (opens in a new tab)2021PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
mT5 (opens in a new tab)2021mT5: A massively multilingual pre-trained text-to-text transformer
CPM-2 (opens in a new tab)2021CPM-2: Large-scale Cost-effective Pre-trained Language Models
T0 (opens in a new tab)2021Multitask Prompted Training Enables Zero-Shot Task Generalization
HyperCLOVA (opens in a new tab)2021What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
Codex (opens in a new tab)2021Evaluating Large Language Models Trained on Code
ERNIE 3.0 (opens in a new tab)2021ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
Jurassic-1 (opens in a new tab)2021Jurassic-1: Technical Details and Evaluation
FLAN (opens in a new tab)2021Finetuned Language Models Are Zero-Shot Learners
MT-NLG (opens in a new tab)2021Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
Yuan 1.0 (opens in a new tab)2021Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning
WebGPT (opens in a new tab)2021WebGPT: Browser-assisted question-answering with human feedback
Gopher (opens in a new tab)2021Scaling Language Models: Methods, Analysis & Insights from Training Gopher
ERNIE 3.0 Titan (opens in a new tab)2021ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
GLaM (opens in a new tab)2021GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
InstructGPT (opens in a new tab)2022Training language models to follow instructions with human feedback
GPT-NeoX-20B (opens in a new tab)2022GPT-NeoX-20B: An Open-Source Autoregressive Language Model
AlphaCode (opens in a new tab)2022Competition-Level Code Generation with AlphaCode
CodeGen (opens in a new tab)2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Chinchilla (opens in a new tab)2022Shows that for a compute budget, the best performances are not achieved by the largest models but by smaller models trained on more data.
Tk-Instruct (opens in a new tab)2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
UL2 (opens in a new tab)2022UL2: Unifying Language Learning Paradigms
PaLM (opens in a new tab)2022PaLM: Scaling Language Modeling with Pathways
OPT (opens in a new tab)2022OPT: Open Pre-trained Transformer Language Models
BLOOM (opens in a new tab)2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
GLM-130B (opens in a new tab)2022GLM-130B: An Open Bilingual Pre-trained Model
AlexaTM (opens in a new tab)2022AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
Flan-T5 (opens in a new tab)2022Scaling Instruction-Finetuned Language Models
Sparrow (opens in a new tab)2022Improving alignment of dialogue agents via targeted human judgements
U-PaLM (opens in a new tab)2022Transcending Scaling Laws with 0.1% Extra Compute
mT0 (opens in a new tab)2022Crosslingual Generalization through Multitask Finetuning
Galactica (opens in a new tab)2022Galactica: A Large Language Model for Science
OPT-IML (opens in a new tab)2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
LLaMA (opens in a new tab)2023LLaMA: Open and Efficient Foundation Language Models
GPT-4 (opens in a new tab)2023GPT-4 Technical Report
PanGu-Σ (opens in a new tab)2023PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing
BloombergGPT (opens in a new tab)2023BloombergGPT: A Large Language Model for Finance
Cerebras-GPT (opens in a new tab)2023Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster
PaLM 2 (opens in a new tab)2023A Language Model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM.