paper

paper

각종 논문들을 읽고 리뷰한 내용들을 모아놓은 곳입니다.

깃허브 마크다운 LaTex 렌더링 이슈로 인헤, 각 설명 문서 당 .pdf와 .md 두 개의 버전으로 올렸습니다.
또한, 아래의 링크에서도 확인해보실 수 있습니다.

🚀TISTORY LINK🚀

Adam: A Method for Stochastic Optimization (Only Implement)

Attention is all you need

BERT: Pre training of Deep Bidirectional Transformers for Language Understanding

Big Bird: Transformers for Longer Sequences

Dense Passage Retrieval for Open-Domain Question Answering

Direct Fact Retrieval from Knowledge Graphs without Entity Linking

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Effective Approaches to Attention based Neural Machine Translation

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

FEVER- a large-scale dataset for Fact Extraction and VERification

Finetuned Language Models Are Zero-Shot Learners

Generation-Augmented Retrieval for Open-Domain Question Answering

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Improving Language Understanding by Generative Pre Training

Language Models are Unsupervised Multitask Learners

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

Mixed Precision Training

Multilingual Language Processing From Bytes

Multitask Prompted Training Enables Zero-Shot Task Generalization

Neural Machine Translation by Jointly Learning to Align and Translate

Query Expansion by Prompting Large Language Models

REALM: Retrieval-Augmented Language Model Pre-Training

REPLUG: Retrieval-Augmented Black-Box Language Models

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Self-Attention with Relative Position Representations

The Natural Language Decathlon- Multitask Learning as Question Answering

Training language models to follow instructions with human feedback

Using the Output Embedding to Improve Language Models