懂AI
ML-Papers-Explained

ML-Papers-Explained

机器学习论文关键概念解析与发展历程

ML-Papers-Explained项目提供机器学习领域重要论文的详细解释。涵盖Transformer到GPT-4等多个里程碑语言模型，剖析论文核心思想、创新点和应用。项目帮助理解技术概念，展示机器学习发展历程，是跟踪AI进展的重要资源。

语言模型自然语言处理深度学习TransformerGPTGithub开源项目
GitHub论文

ML Papers Explained

Explanations to key concepts in ML

Language Models

PaperDateDescription
TransformerJune 2017An Encoder Decoder model, that introduced multihead attention mechanism for language translation task.
ElmoFebruary 2018Deep contextualized word representations that captures both intricate aspects of word usage and contextual variations across language contexts.
Marian MTApril 2018A Neural Machine Translation framework written entirely in C++ with minimal dependencies, designed for high training and translation speed.
GPTJune 2018A Decoder only transformer which is autoregressively pretrained and then finetuned for specific downstream tasks using task-aware input transformations.
BERTOctober 2018Introduced pre-training for Encoder Transformers. Uses unified architecture across different tasks.
Transformer XLJanuary 2019Extends the original Transformer model to handle longer sequences of text by introducing recurrence into the self-attention mechanism.
XLMJanuary 2019Proposes two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective.
GPT 2February 2019Demonstrates that language models begin to learn various language processing tasks without any explicit supervision.
Sparse TransformerApril 2019Introduced sparse factorizations of the attention matrix to reduce the time and memory consumption to O(n√ n) in terms of sequence lengths.
UniLMMay 2019Utilizes a shared Transformer network and specific self-attention masks to excel in both language understanding and generation tasks.
XLNetJune 2019Extension of the Transformer-XL, pre-trained using a new method that combines ideas from AR and AE objectives.
RoBERTaJuly 2019Built upon BERT, by carefully optimizing hyperparameters and training data size to improve performance on various language tasks .
Sentence BERTAugust 2019A modification of BERT that uses siamese and triplet network structures to derive sentence embeddings that can be compared using cosine-similarity.
CTRLSeptember 2019A 1.63B language model that can generate text conditioned on control codes that govern style, content, and task-specific behavior, allowing for more explicit control over text generation.
Tiny BERTSeptember 2019Uses attention transfer, and task specific distillation for distilling BERT.
ALBERTSeptember 2019Presents certain parameter reduction techniques to lower memory consumption and increase the training speed of BERT.
Distil BERTOctober 2019Distills BERT on very large batches leveraging gradient accumulation, using dynamic masking and without the next sentence prediction objective.
T5October 2019A unified encoder-decoder framework that converts all text-based language problems into a text-to-text format.
BARTOctober 2019An Encoder-Decoder pretrained to reconstruct the original text from corrupted versions of it.
XLM-RobertaNovember 2019A multilingual masked language model pre-trained on text in 100 languages, shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of crosslingual transfer tasks.
XLM-RobertaNovember 2019A multilingual masked language model pre-trained on text in 100 languages, shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of crosslingual transfer tasks.
PegasusDecember 2019A self-supervised pre-training objective for abstractive text summarization, proposes removing/masking important sentences from an input document and generating them together as one output sequence.
ReformerJanuary 2020Improves the efficiency of Transformers by replacing dot-product attention with locality-sensitive hashing (O(Llog L) complexity), using reversible residual layers to store activations only once, and splitting feed-forward layer activations into chunks, allowing it to perform on par with Transformer models while being much more memory-efficient and faster on long sequences.
mBARTJanuary 2020A multilingual sequence-to-sequence denoising auto-encoder that pre-trains a complete autoregressive model on large-scale monolingual corpora across many languages using the BART objective, achieving significant performance gains in machine translation tasks.
UniLMv2February 2020Utilizes a pseudo-masked language model (PMLM) for both autoencoding and partially autoregressive language modeling tasks,significantly advancing the capabilities of language models in diverse NLP tasks.
ELECTRAMarch 2020Proposes a sample-efficient pre-training task called replaced token detection, which corrupts input by replacing some tokens with plausible alternatives and trains a discriminative model to predict whether each token was replaced or no.
FastBERTApril 2020A speed-tunable encoder with adaptive inference time having branches at each transformer output to enable early outputs.
MobileBERTApril 2020Compressed and faster version of the BERT, featuring bottleneck structures, optimized attention mechanisms, and knowledge transfer.
LongformerApril 2020Introduces a linearly scalable attention mechanism, allowing handling texts of exteded length.
GPT 3May 2020Demonstrates that scaling up language models greatly improves task-agnostic, few-shot performance.
DeBERTaJune 2020Enhances BERT and RoBERTa through disentangled attention mechanisms, an enhanced mask decoder, and virtual adversarial training.
DeBERTa v2June 2020Enhanced version of the DeBERTa featuring a new vocabulary, nGiE integration, optimized attention mechanisms, additional model sizes, and improved tokenization.
T5 v1.1July 2020An enhanced version of the original T5 model, featuring improvements such as GEGLU activation, no dropout in pre-training, exclusive pre-training on C4, no parameter sharing between embedding and classifier layers.
mT5October 2020A multilingual variant of T5 based on T5 v1.1, pre-trained on a new Common Crawl-based dataset covering 101 languages (mC4).
CodexJuly 2021A GPT language model finetuned on publicly available code from GitHub.
FLANSeptember 2021An instruction-tuned language model developed through finetuning on various NLP datasets described by natural language instructions.
T0October 2021A fine tuned encoder-decoder model on a multitask mixture covering a wide variety of tasks, attaining strong zero-shot performance on several standard datasets.
DeBERTa V3November 2021Enhances the DeBERTa architecture by introducing replaced token detection (RTD) instead of mask language modeling (MLM), along with a novel gradient-disentangled embedding sharing method, exhibiting superior performance across various natural language understanding tasks.
WebGPTDecember 2021A fine-tuned GPT-3 model utilizing text-based web browsing, trained via imitation learning and human feedback, enhancing its ability to answer long-form questions with factual accuracy.
GopherDecember 2021Provides a comprehensive analysis of the performance of various Transformer models across different scales upto 280B on 152 tasks.
LaMDAJanuary 2022Transformer based models specialized for dialog, which are pre-trained on public dialog data and web text.
Instruct GPTMarch 2022Fine-tuned GPT using supervised learning (instruction tuning) and reinforcement learning from human feedback to align with user intent.
CodeGenMarch 2022An LLM trained for program synthesis using input-output examples and natural language descriptions.
ChinchillaMarch 2022Investigated the optimal model size and number of tokens for training a transformer LLM within a given compute budget (Scaling Laws).
PaLMApril 2022A 540-B parameter, densely activated, Transformer, trained using Pathways, (ML system that enables highly efficient training across multiple TPU Pods).
GPT-NeoX-20BApril 2022An autoregressive LLM trained on the Pile, and the largest dense model that had publicly available weights at the time of submission.
OPTMay 2022A suite of decoder-only pre-trained transformers with parameter ranges from 125M to 175B. OPT-175B being comparable to GPT-3.
Flan T5, Flan PaLMOctober 2022Explores instruction fine tuning with a particular focus on scaling the number of tasks, scaling the model size, and fine tuning on chain-of-thought data.
BLOOMNovember 2022A 176B-parameter open-access decoder-only transformer, collaboratively developed by hundreds of researchers, aiming to democratize LLM technology.
BLOOMZ, mT0November 2022Applies Multitask prompted fine tuning to the pretrained multilingual models on English tasks with English prompts to attain task generalization to non-English languages that appear only in the pretraining corpus.
GalacticaNovember 2022An LLM trained on scientific data thus specializing in scientific knowledge.
ChatGPTNovember 2022An interactive model designed to engage in conversations, built on top of GPT 3.5.
Self InstructDecember 2022A framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations.
LLaMAFebruary 2023A collection of foundation LLMs by Meta ranging from 7B to 65B parameters, trained using publicly available datasets exclusively.
ToolformerFebruary 2023An LLM trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction.
AlpacaMarch 2023A fine-tuned LLaMA 7B model, trained on instruction-following demonstrations generated in the style of self-instruct using text-davinci-003.
GPT 4March 2023A multimodal transformer model pre-trained to predict the next token in a document, which can accept image and text inputs and produce text outputs.
VicunaMarch 2023A 13B LLaMA chatbot fine tuned on user-shared conversations collected from ShareGPT, capable of generating more detailed and well-structured answers compared to Alpaca.
BloombergGPTMarch 2023A 50B language model train on general purpose and domain specific data to support a wide range of tasks within the financial industry.

编辑推荐精选

iTerms

iTerms

企业专属的AI法律顾问

iTerms是法大大集团旗下法律子品牌，基于最先进的大语言模型(LLM)、专业的法律知识库和强大的智能体架构，帮助企业扫清合规障碍，筑牢风控防线，成为您企业专属的AI法律顾问。

SimilarWeb流量提升

SimilarWeb流量提升

稳定高效的流量提升解决方案，助力品牌曝光

稳定高效的流量提升解决方案，助力品牌曝光

Sora2视频免费生成

Sora2视频免费生成

最新版Sora2模型免费使用，一键生成无水印视频

最新版Sora2模型免费使用，一键生成无水印视频

Transly

Transly

实时语音翻译/同声传译工具

Transly是一个多场景的AI大语言模型驱动的同声传译、专业翻译助手，它拥有超精准的音频识别翻译能力，几乎零延迟的使用体验和支持多国语言可以让你带它走遍全球，无论你是留学生、商务人士、韩剧美剧爱好者，还是出国游玩、多国会议、跨国追星等等，都可以满足你所有需要同传的场景需求，线上线下通用，扫除语言障碍，让全世界的语言交流不再有国界。

讯飞绘文

讯飞绘文

选题、配图、成文，一站式创作，让内容运营更高效

讯飞绘文，一个AI集成平台，支持写作、选题、配图、排版和发布。高效生成适用于各类媒体的定制内容，加速品牌传播，提升内容营销效果。

热门AI辅助写作AI工具讯飞绘文内容运营AI创作个性化文章多平台分发AI助手
TRAE编程

TRAE编程

AI辅助编程，代码自动修复

Trae是一种自适应的集成开发环境（IDE），通过自动化和多元协作改变开发流程。利用Trae，团队能够更快速、精确地编写和部署代码，从而提高编程效率和项目交付速度。Trae具备上下文感知和代码自动完成功能，是提升开发效率的理想工具。

AI工具TraeAI IDE协作生产力转型热门
商汤小浣熊

商汤小浣熊

最强AI数据分析助手

小浣熊家族Raccoon，您的AI智能助手，致力于通过先进的人工智能技术，为用户提供高效、便捷的智能服务。无论是日常咨询还是专业问题解答，小浣熊都能以快速、准确的响应满足您的需求，让您的生活更加智能便捷。

imini AI

imini AI

像人一样思考的AI智能体

imini 是一款超级AI智能体，能根据人类指令，自主思考、自主完成、并且交付结果的AI智能体。

Keevx

Keevx

AI数字人视频创作平台

Keevx 一款开箱即用的AI数字人视频创作平台，广泛适用于电商广告、企业培训与社媒宣传，让全球企业与个人创作者无需拍摄剪辑，就能快速生成多语言、高质量的专业视频。

即梦AI

即梦AI

一站式AI创作平台

提供 AI 驱动的图片、视频生成及数字人等功能，助力创意创作

下拉加载更多