[Intensive reading of NLP classic papers] Language Models are Few-Shot Learners - Code World

[Intensive reading of NLP classic papers] Language Models are Few-Shot Learners

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[Intensive reading of NLP classic papers] Language Models are Few-Shot Learners

[Intensive reading of NLP classic papers] Language Models are Unsupervised Multitask Learners

(GPT3) Language Models are Few-Shot Learners Paper Reading

[Paper Reading] Language Models are Few-Shot Learners (GPT-3)

GPT-3(Language Models are Few-shot Learners)简介

[Intensive reading of NLP classic papers] Improving Language Understanding by Generative Pre-Training

[NLP classic paper intensive reading] LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS

【Intensive reading of papers】Few-shot Named Entity Recognition with Self-describing Networks

[Intensive reading of papers] GPT-NER: Named Entity Recognition via Large Language Models

[NLP classic paper intensive reading] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

[NLP classic paper intensive reading] Attention Is All You Need

[NLP 고전 논문 집중 읽기] Language Models are Few-Shot Learners

[Intensive reading of papers] Mask-guided BERT for Few Shot Text Classification

Intensive Reading of SIMBAR Papers

Intensive reading of GAN papers

Intensive Reading of Transformer Papers

[Intensive reading of papers] HugNLP: A Unified and Comprehensive Library for Natural Language Processing

ChatGPT 论文：Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models (一)

Intensive Reading of YOLO Series Papers

Super detailed interpretation of classic neural network papers (6) - DenseNet study notes (translation + intensive reading + code reproduction)

[NLP classic paper intensive reading] Prefix-Tuning: Optimizing Continuous Prompts for Generation

【Intensive reading of papers】MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER

Pre-training of large language models [6]: Detailed explanation of the definition principle of Chain-of-thought (CoT), Zero-shot CoT, Few-shot CoT and application on LLM

Super detailed interpretation of classic neural network papers (5) - ResNet (residual network) study notes (translation + intensive reading + code reproduction)

Intensive reading of deep learning papers [8]: ParseNet

Intensive reading of deep learning papers [7]: nnUNet

[Intensive reading of papers] Fast R-CNN

[CVPR2022] Intensive Reading of QueryDet Papers

Target Detection - Intensive Reading of FPN Papers

[Intensive reading of papers] DELS-MVS

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