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Deep-Learning-Paper

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These are papers that I read and reviewed related to NLP, CV, and Deep Learning 😉 You can check paper links and my reviews 😊

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# NLP, CV, Deep Learning Paper Reading Table

I read these papers that are related to NLP and Deep Learning. Here are various papers from basic to advanced. 😊
In addition, you can check my Korean paper reviews by clicking the link attached to the table. 😉

You can see more paper reviews, code implementation, and mathematics descriptions in my [blog](https://cartinoe5930.tistory.com/) <- click here

## My Insight 🧐

I write several articles to explain in detail some Deep Learning technologies. These articles can be found in the table below.

|Title|Blog link|
|:---:|---|
|How has scaling law developed in NLP? 🤔|https://cartinoe5930.tistory.com/entry/How-has-scaling-law-developed-in-NLP-%F0%9F%A4%94-NLP%EC%97%90%EC%84%9C-scaling-law%EB%8A%94-%EC%96%B4%EB%96%BB%EA%B2%8C-%EB%B0%9C%EC%A0%84%EB%90%98%EC%97%88%EC%9D%84%EA%B9%8C|
|Closed-source🔒? Open-source🔓? What is that?? 🤨🤔|https://cartinoe5930.tistory.com/entry/The-hopes-of-researchers-Open-source-%F0%9F%A4%97-%EC%97%B0%EA%B5%AC%EC%9E%90%EB%93%A4%EC%9D%98-%ED%9D%AC%EB%A7%9D-Open-source-%F0%9F%A4%97|
|Context window of LM, should it be long? Should it be short? 📏🤨|https://cartinoe5930.tistory.com/entry/LM%EC%9D%98-context-window-%EA%B8%B8%EC%96%B4%EC%95%BC-%ED%95%A0%EA%B9%8C-%EC%A7%A7%EC%95%84%EC%95%BC-%ED%95%A0%EA%B9%8C-%F0%9F%93%8F%F0%9F%A4%A8|
|What is the most optimal way to evaluate LM? 😎|https://cartinoe5930.tistory.com/entry/LM%EC%9D%84-%EA%B0%80%EC%9E%A5-%EC%B5%9C%EC%A0%81%EC%9C%BC%EB%A1%9C-%ED%8F%89%EA%B0%80%ED%95%A0-%EC%88%98-%EC%9E%88%EB%8A%94-%EB%B0%A9%EB%B2%95%EC%9D%80-%EB%AC%B4%EC%97%87%EC%9D%BC%EA%B9%8C-%F0%9F%98%8E|
|The performance of ChatGPT is getting worse?!?!? 😲😲|https://cartinoe5930.tistory.com/entry/ChatGPT%EC%9D%98-%EC%84%B1%EB%8A%A5%EC%9D%B4-%EC%95%88-%EC%A2%8B%EC%95%84%EC%A7%80%EA%B3%A0-%EC%9E%88%EB%8B%A4%EA%B5%AC-%F0%9F%98%B2%F0%9F%98%B2|
|You can fine-tune too! with PEFT 🤗|https://cartinoe5930.tistory.com/entry/%EB%8B%B9%EC%8B%A0%EB%8F%84-Fine-tuning-%ED%95%A0-%EC%88%98-%EC%9E%88%EC%8A%B5%EB%8B%88%EB%8B%A4-with-PEFT-%F0%9F%A4%97|
|Let's think step by step like humans! 🧠🤔|https://cartinoe5930.tistory.com/entry/%ED%95%9C-%EB%8B%A8%EA%B3%84-%ED%95%9C-%EB%8B%A8%EA%B3%84%EC%94%A9-%EC%9D%B8%EA%B0%84%EC%B2%98%EB%9F%BC-%EC%83%9D%EA%B0%81%ED%95%B4%EB%B3%B4%EC%9E%90-%F0%9F%A7%A0%F0%9F%A4%94|
|Development process of fine-tuning method!! From fine-tuning to RLHF 🦖➡️🧑|https://cartinoe5930.tistory.com/entry/Fine-tuning-method%EC%9D%98-%EC%A7%84%ED%99%94-%EA%B3%BC%EC%A0%95-%F0%9F%A6%96%E2%9E%A1%EF%B8%8F%F0%9F%A7%91|
|It's time to fine-tune ChatGPT!! ⏰|https://cartinoe5930.tistory.com/entry/%EC%9D%B4%EC%A0%9C%EB%8A%94-ChatGPT%EB%A5%BC-fine-tuning-%ED%95%A0-%EC%8B%9C%EA%B0%84-%E2%8F%B0|
|Noise makes LLM better! - NEFTune 😉|https://cartinoe5930.tistory.com/entry/Noise-makes-LLM-better-NEFTune-%F0%9F%98%89|

## Natural Language Processing

### Word Embedding & Neural Networks
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|Embedding Matrix|https://wikidocs.net/book/2155|https://cartinoe5930.tistory.com/entry/Embedding-Matrix-%ED%95%99%EC%8A%B5|
|LSTM: Long-Short Term Memory|https://colah.github.io/posts/2015-08-Understanding-LSTMs/|https://cartinoe5930.tistory.com/entry/%EC%95%8C%EA%B8%B0-%EC%89%BD%EA%B2%8C-LSTM-networks-%EC%9D%B4%ED%95%B4%ED%95%98%EA%B8%B0|
|GRU: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation|https://arxiv.org/abs/1406.1078|https://cartinoe5930.tistory.com/entry/GRU-Empirical-Evaluation-of-Gated-Recurrent-Neural-Networks-on-Sequence-Modeling-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|LSTM vs. GRU: Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling|https://arxiv.org/abs/1412.3555|https://cartinoe5930.tistory.com/entry/LSTM-vs-GRU-%EB%AD%90%EA%B0%80-%EB%8D%94-%EB%82%98%EC%9D%84%EA%B9%8C-Empirical-Evaluation-of-Gated-Recurrent-Neural-Networks-on-Sequence-Modeling-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|

### Language Models🤖
#### Basic📖
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|Transformer: Attention Is All You Need|https://arxiv.org/abs/1706.03762|https://cartinoe5930.tistory.com/entry/Transformer-Attention-Is-All-You-Need-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|ELMo: Deep contextualized word representations|https://arxiv.org/abs/1802.05365|https://cartinoe5930.tistory.com/entry/Pre-trained-Language-Modeling-paper-reading1-ELMo-Deep-contextualized-word-representations|
|BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding|https://arxiv.org/abs/1810.04805|https://cartinoe5930.tistory.com/entry/Pre-trained-Language-Modeling-paper-reading2-BERT-Pre-training-of-Deep-Bidirectional-Transformers-for-Language-Understanding|
|GPT-1: Improving Language Understanding by Generative Pre-Training|https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf|https://cartinoe5930.tistory.com/entry/Pre-trained-Language-Modeling-paper-reading3-GPT-1-Improving-Language-Understanding-by-Generative-Pre-Training|
|GPT-2: Language Models are Unsupervised Multitask Learners|https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf|https://cartinoe5930.tistory.com/entry/GPT-2-Language-Models-are-Unsupervised-Multitask-Learners-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|GPT-3: Language Models are Few-Shot Learners|https://cartinoe5930.tistory.com/entry/GPT-3-Language-Models-are-Few-Shot-Learners-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|https://cartinoe5930.tistory.com/entry/GPT-3-Language-Models-are-Few-Shot-Learners-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context|https://arxiv.org/abs/1901.02860|https://cartinoe5930.tistory.com/entry/Transformer-XL-Attentive-Language-Models-Beyond-a-Fixed-Length-Context-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Sparse Transformers: Generating Long Sequences with Sparse Transformers|https://arxiv.org/abs/1904.10509|https://cartinoe5930.tistory.com/entry/Sparse-Transformers-Generating-Long-Sequence-with-Sparse-Transformers-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|XLNET: Generalized Autoregressive Pretraining for Language Understanding|https://arxiv.org/abs/1906.08237|https://cartinoe5930.tistory.com/entry/XLNet-Generalized-Autoregressive-Pretraining-for-Language-Understanding-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|SpanBERT: Improving Pre-training by Representing and Predicting Spans|https://arxiv.org/abs/1907.10529|https://cartinoe5930.tistory.com/entry/SpanBERT-Improving-Pre-training-by-Representing-and-Predicting-Spans-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|RoBERTa: A Robustly Optimized BERT Pre-training Approach|https://arxiv.org/abs/1907.11692|https://cartinoe5930.tistory.com/entry/RoBERTa-A-Robustly-Optimized-BERT-Pretraining-Approach-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks|https://arxiv.org/abs/1908.10084|https://cartinoe5930.tistory.com/entry/Sentence-BERT-Sentence-Embeddings-using-Siamese-BERT-Networks-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|ALBERT: A Lite BERT for Self-supervised Learning of Language Representations|https://arxiv.org/abs/1909.11942|https://cartinoe5930.tistory.com/entry/ALBERT-A-Lite-BERT-for-Self-supervised-Learning-of-Language-Representations-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension|https://arxiv.org/abs/1910.13461|https://cartinoe5930.tistory.com/entry/BART-Denoising-Sequence-to-Sequence-Pre-training-for-Natural-Language-Generation-Translation-and-Comprehension-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Pre-LN Transformer: On Layer Normalization in the Transformer Architecture|https://arxiv.org/abs/2002.04745|https://cartinoe5930.tistory.com/entry/Pre-LN-Transformer-On-Layer-Normalization-in-the-Transformer-Architecture-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|ELECTRA: Pre-training Text Encoders as Discriminators rather than Generators|https://arxiv.org/abs/2003.10555|https://cartinoe5930.tistory.com/entry/ELECTRA-Pre-training-Text-Encoders-as-Discriminators-rather-than-Generators|
|Longformer: The Long-Document Transformer|https://arxiv.org/abs/2004.05150|https://cartinoe5930.tistory.com/entry/Longformer-The-Long-Document-Transformer-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|BigBird: Transformers for Longer Sequences|https://arxiv.org/abs/2007.14062|https://cartinoe5930.tistory.com/entry/BigBird-Transformers-for-Longer-Sequences-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|WebGPT: Browser-assisted question-answering with human feedback|https://arxiv.org/abs/2112.09332|https://cartinoe5930.tistory.com/entry/WebGPT-Browser-assisted-question-answering-with-human-feedback-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|OPT: Open Pre-trained Transformer Language Models|https://arxiv.org/abs/2205.01068|https://cartinoe5930.tistory.com/entry/OPT-Open-Pre-trained-Transformer-Language-Models-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Mamba: Linear-Time Sequence Modeling with Selective State Spaces|https://arxiv.org/abs/2312.00752|No plan!|

#### Efficient Models💸
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|TinyBERT: Distilling BERT for Natural Language Understanding|https://arxiv.org/abs/1909.10351|https://cartinoe5930.tistory.com/entry/TinyBERT-Distilling-BERT-for-Natural-Language-Understanding-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|DistilBERT: a distilled version of BERT|https://arxiv.org/abs/1910.01108|https://cartinoe5930.tistory.com/entry/DistilBERT-a-distilled-version-of-BERT-smaller-faster-cheaper-and-lighter-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|It's Not Just Size That Matters: Small Language Models are Also Few-Shot Learners(PET 응용)|https://arxiv.org/abs/2009.07118|https://cartinoe5930.tistory.com/entry/Its-Not-Just-Size-That-Matters-Small-Language-Models-Are-Also-Few-Shot-Learners-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|

#### Open-source Language Model(Scaling law)🤗
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|Chinchilla: Training Compute-Optimal Large Language Models|https://arxiv.org/abs/2203.15556|https://cartinoe5930.tistory.com/entry/%EC%A7%80%EA%B8%88-%EA%B9%8C%EC%A7%80%EC%9D%98-LM-Scaling-Law%EC%97%90%EB%8A%94-%EB%AC%B8%EC%A0%9C%EC%A0%90%EC%9D%B4-%EC%9E%88%EB%8B%A4-%F0%9F%98%B6%E2%80%8D%F0%9F%8C%AB%EF%B8%8F-Chinchilla-Training-Compute-Optimal-Large-Language-Models-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling|https://arxiv.org/abs/2304.01373|No plan!|
|LIMA: Less Is More for Alignment|https://arxiv.org/abs/2305.11206|https://cartinoe5930.tistory.com/entry/LIMA-Less-Is-More-for-Alignment-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|LLaMA: Open and Efficient Foundation Language Models|https://arxiv.org/abs/2302.13971|https://cartinoe5930.tistory.com/entry/LLaMA-Open-and-Efficient-Foundation-Language-Models-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|WizardLM: Empowering Large Language Models to Follow Complex Instructions|https://arxiv.org/abs/2304.12244|https://cartinoe5930.tistory.com/entry/Open-domain-instruction%EC%9D%98-%ED%9A%A8%EA%B3%BC-%F0%9F%AA%84-WizardLM-Empowering-Large-Language-Models-to-Follow-Complex-Instructions-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|WizardCoder: Empowering Code Large Language Models with Evol-Instruct|https://arxiv.org/abs/2306.08568|https://huggingface.co/WizardLM/WizardCoder-15B-V1.0|
|WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct|https://arxiv.org/abs/2308.09583|https://huggingface.co/WizardLM/WizardMath-70B-V1.0|
|Alpaca: A Strong, Replicable Instruction-Following Model|https://crfm.stanford.edu/2023/03/13/alpaca.html|https://cartinoe5930.tistory.com/entry/Alpaca-A-Strong-Replicable-Instruction-Following-Model-%EB%A6%AC%EB%B7%B0|
|Vicuna: An Open-Source Chatbot Impressing GPT-4|https://lmsys.org/blog/2023-03-30-vicuna/|https://cartinoe5930.tistory.com/entry/Vicuna-An-Open-Source-Chatbot-Impressing-GPT-4-%EB%A6%AC%EB%B7%B0|
|Koala: A Dialogue Model for Academic Research|https://bair.berkeley.edu/blog/2023/04/03/koala/|https://cartinoe5930.tistory.com/entry/%EC%A4%91%EC%9A%94%ED%95%9C-%EA%B1%B4-%EA%BA%BE%EC%9D%B4%EC%A7%80-%EC%95%8A%EB%8A%94-high-quality-data-Koala%F0%9F%90%A8-A-Dialogue-Model-for-Academic-Researc|
|Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data|https://arxiv.org/abs/2304.01196|https://cartinoe5930.tistory.com/entry/%F0%9F%90%B2Baize-An-Open-Source-Chat-Model-with-Parameter-Efficient-Tuning-on-Self-Chat-Data-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Scaling Data-Constrained Language Models|https://arxiv.org/abs/2305.16264|https://www.youtube.com/watch?v=TK0-sitkCMw&pp=ygUgaHR0cHM6Ly9hcnhpdi5vcmcvYWJzLzIzMDUuMTYyNjQ%3D|
|Falcon & RefinedWeb|https://arxiv.org/abs/2306.01116|https://cartinoe5930.tistory.com/entry/Open-LLM-Leaderboard%EB%A5%BC-%ED%9C%A9%EC%93%B4-Falcon%F0%9F%A6%85-LLM-Falcon-RefinedWeb|
|Orca: Progressive Learning from Complex Explanation Traces of GPT-4|https://arxiv.org/pdf/2306.02707|https://cartinoe5930.tistory.com/entry/%F0%9F%90%ACOrca-Progressive-Learning-from-Complex-Explanation-Traces-of-GPT-4-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|phi-1: Text Books Are All You Need|https://arxiv.org/abs/2306.11644|https://cartinoe5930.tistory.com/entry/%ED%95%84%EC%9A%94%ED%95%9C-%EA%B1%B4-%EC%98%A4%EC%A7%81-%EA%B5%90%EA%B3%BC%EC%84%9C-%EC%88%98%EC%A4%80%EC%9D%98-%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%BF%90-%F0%9F%93%96-phi-1-Textbooks-Are-All-You-Need-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|AlpaGasus: Training a Better Alpaca with Fewer Data|https://arxiv.org/abs/2307.08701|Will be uploaded later!|
|Llama 2: Open Foundation and Fine-Tuned Chat Models|https://arxiv.org/abs/2307.09288|https://cartinoe5930.tistory.com/entry/The-hopes-of-researchers-Open-source-%F0%9F%A4%97-%EC%97%B0%EA%B5%AC%EC%9E%90%EB%93%A4%EC%9D%98-%ED%9D%AC%EB%A7%9D-Open-source-%F0%9F%A4%97|
|Platypus: Quick, Cheap, and Powerful Refinement of LLMs|https://arxiv.org/abs/2308.07317|Will be uploaded later!|
|Code Llama: Open Foundation Models for Code|https://arxiv.org/abs/2308.12950|No plan|
|FLM-101B: An Open LLM and How to Train It with $100K Budget|https://arxiv.org/pdf/2309.03852|No plan!|
|Textbooks are All You Need II: phi-1.5 technical report|https://arxiv.org/abs/2309.05463|https://huggingface.co/microsoft/phi-1_5|
|OpenChat: Advancing Open-Source Language Models with Mixed-Quality Data|https://arxiv.org/abs/2309.11235|https://github.com/imoneoi/openchat|
|Mistral 7B|https://arxiv.org/abs/2310.06825|https://mistral.ai/news/announcing-mistral-7b/|
|Prometheus: Inducing Fine-grained Evaluation Capability in Language Models|https://arxiv.org/abs/2310.08491|https://huggingface.co/papers/2310.08491#652a8e7f30355beba68c1be6|
|Zephyr: Direct Distillation of LM Alignment|https://arxiv.org/abs/2310.16944|https://www.youtube.com/watch?v=TkZBg3mKsIo|
|Orca2: Teaching Small Language Models How to Reason|https://arxiv.org/abs/2311.11045|https://www.microsoft.com/en-us/research/blog/orca-2-teaching-small-language-models-how-to-reason/|
|The Falcon Series of Open Language Models|https://arxiv.org/abs/2311.16867|No plan!|
|SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling|https://arxiv.org/abs/2312.15166|No plan!|

#### Large Language Models(LLMs)💣
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|LaMDA: Language Models for Dialog Applications|blog: https://ai.googleblog.com/2022/01/lamda-towards-safe-grounded-and-high.html, paper: https://arxiv.org/abs/2201.08239|https://cartinoe5930.tistory.com/entry/%EA%B5%AC%EA%B8%80%EC%9D%98-%EC%B5%9C%EA%B0%95-%EC%B1%97%EB%B4%87-LaMDA%EC%97%90-%EB%8C%80%ED%95%B4-%EC%95%8C%EC%95%84%EB%B3%B4%EC%9E%90-Language-Models-for-Dialog-Applications-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|PaLM: Scaling Language Modeling with Pathways|blog: https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html, paper: https://arxiv.org/abs/2204.02311|1: https://cartinoe5930.tistory.com/entry/LaMDA%EC%9D%98-%EB%92%A4%EB%A5%BC-%EC%9E%87%EB%8A%94-Pathways%EB%A5%BC-%ED%99%9C%EC%9A%A9%ED%95%9C-%EC%B4%88%EA%B1%B0%EB%8C%80-%EC%96%B8%EC%96%B4-%EB%AA%A8%EB%8D%B8-PaLM-%EB%A6%AC%EB%B7%B0, 2: https://cartinoe5930.tistory.com/entry/LaMDA%EC%9D%98-%EB%92%A4%EB%A5%BC-%EC%9E%87%EB%8A%94-Pathways%EB%A5%BC-%EC%82%AC%EC%9A%A9%ED%95%9C-%EC%B4%88%EA%B1%B0%EB%8C%80-%EC%96%B8%EC%96%B4-%EB%AA%A8%EB%8D%B8-PaLM-%EB%A6%AC%EB%B7%B02|
|GPT-4: Technical Review|blog: https://openai.com/research/gpt-4, paper: https://arxiv.org/abs/2303.08774|https://cartinoe5930.tistory.com/entry/GPT-4-Techinal-Report-Review|
|Gemini: A Family of Highly Capable Multimodal Models|https://arxiv.org/abs/2312.11805|No plan!|
|AlphaCode 2 Technical Report|https://storage.googleapis.com/deepmind-media/AlphaCode2/AlphaCode2_Tech_Report.pdf|No plan!|

### Fine-tuning
#### Instruction-tuning🧑‍🏫
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|FLAN: Fine-tuned Language Models are Zero-shot Learners|https://arxiv.org/abs/2109.01652|https://cartinoe5930.tistory.com/entry/FLAN-Fine-tuned-Language-Models-are-Zero-shot-Learners-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|T0: Multitask Prompted Training Enables Zero-shot Task Generalization|https://arxiv.org/abs/2110.08207|https://cartinoe5930.tistory.com/entry/T0-Multitask-Prompted-Training-Enables-Zero-shot-Task-Generalization-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Super-Natural Instructions: Generalization via Declarative Instructions on 1600+ NLP Tasks|https://arxiv.org/abs/2204.07705|https://cartinoe5930.tistory.com/entry/Super-Natural-Instructions-Generalization-via-Declarative-Instructions-on-1600-NLP-Tasks-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Unnatural Instructions: Tuning Language Models with (Almost) Not Human Labor|https://arxiv.org/abs/2212.09689|Will be uploaded later!|
|Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-shot Learners|https://arxiv.org/abs/2210.02969|https://cartinoe5930.tistory.com/entry/Guess-the-Instruction-Flipped-Learning-Makes-Language-Models-Stronger-Zero-shot-Learners-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Scaling Instruction-Finetuned Language Models|https://arxiv.org/abs/2210.11416|https://cartinoe5930.tistory.com/entry/Scaling-Instruction-Finetuned-Language-Models-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Exploring the Benefits of Training Expert Language Models over Instruction Tuning|https://arxiv.org/abs/2302.03202|https://cartinoe5930.tistory.com/entry/Exploring-the-Benefits-of-Training-Expert-Language-Models-over-Instruction-Tuning-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|ICIL: In-Context Instruction Learning|https://arxiv.org/abs/2302.14691|https://cartinoe5930.tistory.com/entry/ICIL-In-Context-Instruction-Learning-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Instruction tuning with GPT-4|https://arxiv.org/abs/2304.03277|https://cartinoe5930.tistory.com/entry/Instruction-Tuning-with-GPT-4-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|FIP: Fixed Input Parameterization for Efficient Prompting|https://aclanthology.org/2023.findings-acl.533.pdf|Will be uploaded later!|
|FlaCuna: unleashin the Problem Solving Power of Vicuna using FLAN Fine-tuning|https://arxiv.org/abs/2307.02053|Will be uploaded later!|
|Maybe Only 0.5% Data Is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning|https://arxiv.org/abs/2305.09246|Will be uploaded later!|
|Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning|https://arxiv.org/abs/2307.03692|Will be uploaded later!|

#### Reinforcement Learning from Human Feedback(RLHF)👥
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|RLHF(Reinforcement Learning from Human Feedback)|https://huggingface.co/blog/rlhf|https://cartinoe5930.tistory.com/entry/%EC%82%AC%EB%9E%8C%EC%9D%98-%ED%94%BC%EB%93%9C%EB%B0%B1%EC%9D%84-%ED%86%B5%ED%95%9C-%EA%B0%95%ED%99%94%ED%95%99%EC%8A%B5-Reinforcement-Learning-from-Human-Feedback-RLHF|
|Red Teaming Language Models with Language Models|https://arxiv.org/abs/2202.03286|https://cartinoe5930.tistory.com/entry/Red-Teaming-Language-Models-with-Language-Models-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|InstructGPT: Training language models to follow instructions with human feedback|https://arxiv.org/abs/2203.02155|https://cartinoe5930.tistory.com/entry/InstructGPT-Training-language-models-to-follow-instructions-with-human-feedback-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Training a helpful and harmless assistant with reinforcement learning from human feedback|https://arxiv.org/abs/2204.05862|https://cartinoe5930.tistory.com/entry/Training-a-helpful-and-harmless-assistant-with-reinforcement-learning-from-human-feedback-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback|https://arxiv.org/abs/2305.14387|Will be uploaded later!|
|ALMoST: Aligning Large Language Models through Synthetic Feedback|https://arxiv.org/abs/2305.13735|https://cartinoe5930.tistory.com/entry/Aligning-Large-Language-Models-through-Synthetic-Feedback-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback|https://arxiv.org/abs/2307.15217|Will be uploaded later!|
|RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback|https://arxiv.org/abs/2309.00267|No plan!|
|SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF|https://arxiv.org/abs/2310.05344|No plan!|
|HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM|https://arxiv.org/abs/2311.09528|No plan!|

#### Efficient-tuning ✨
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|Adapter: Parameter-Efficient learning for NLP|https://arxiv.org/abs/1902.00751|https://cartinoe5930.tistory.com/entry/%EB%8B%B9%EC%8B%A0%EB%8F%84-Fine-tuning-%ED%95%A0-%EC%88%98-%EC%9E%88%EC%8A%B5%EB%8B%88%EB%8B%A4-with-PEFT-%F0%9F%A4%97|
|Prefix-Tuning: Optimizing Continuous Prompts for Generation|https://arxiv.org/abs/2101.00190|https://cartinoe5930.tistory.com/entry/%EB%8B%B9%EC%8B%A0%EB%8F%84-Fine-tuning-%ED%95%A0-%EC%88%98-%EC%9E%88%EC%8A%B5%EB%8B%88%EB%8B%A4-with-PEFT-%F0%9F%A4%97|
|LoRA: Low-Rank Adaptation of Large Language Models|https://arxiv.org/abs/2106.09685|https://cartinoe5930.tistory.com/entry/%EB%8B%B9%EC%8B%A0%EB%8F%84-Fine-tuning-%ED%95%A0-%EC%88%98-%EC%9E%88%EC%8A%B5%EB%8B%88%EB%8B%A4-with-PEFT-%F0%9F%A4%97|
|Towards a Unified View of Parameter-Efficient Transfer Learning|https://arxiv.org/abs/2110.04366|Will be uploaded later!|
|UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning|https://arxiv.org/abs/2110.07577|Will be uploaded later!|
|(IA)^3: Few-Shot Parameter-Efficient Fine-TUning is Better and Cheaper than In-Context Learning|https://arxiv.org/abs/2205.05638|Will be uploaded later!|
|QLoRA: Efficient Fine-tuning of Quantized LLMs|https://arxiv.org/abs/2305.14314|Will be uploaded later!|
|Stack More Layers Differently: High-Rank Training Through Low-Rank Updates|https://arxiv.org/abs/2307.05695|Will be uploaded later!|
|LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition|https://arxiv.org/abs/2307.13269|Will be uploaded later!|

### Dataset 💫
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|Instruction Mining: High-quality Instruction Data Selection for Large Language Models|https://arxiv.org/abs/2307.06290|No plan!|
|SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization|https://arxiv.org/abs/2212.10465|No plan!|
|MoDS: Model-oriented Data Selection for Instruction Tuning|https://arxiv.org/abs/2311.15653|No plan!|
|Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models|https://arxiv.org/abs/2312.06585|No plan!|
|Magicoder: Source Code Is All You Need|https://arxiv.org/abs/2312.02120|No plan!|
|WaveCoder: Widespread and Versatile Enhanced Instruction Tuning with Refined Data Generation|https://arxiv.org/abs/2312.14187|No plan!|
|What Makes Good Data for Alignment: A Comprehensive Study of Automatic Data Selection in Instruction Tuning|https://arxiv.org/abs/2312.15685|No plan!|

### Prompt Engineering 👨‍🔧
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|What is the 'Prompt Engineering'?|See my blog!|https://cartinoe5930.tistory.com/entry/Prompt-Engineering%EC%9D%B4-%EB%AC%B4%EC%97%87%EC%9D%BC%EA%B9%8C|
|CoT: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models|blog: https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html, paper: https://arxiv.org/abs/2201.11903|https://cartinoe5930.tistory.com/entry/LM%EC%9D%B4-%EC%82%AC%EB%9E%8C%EA%B3%BC-%EC%9C%A0%EC%82%AC%ED%95%9C-%EC%83%9D%EA%B0%81-%ED%94%84%EB%A1%9C%EC%84%B8%EC%8A%A4%EB%A5%BC-%EA%B0%80%EC%A7%80%EA%B2%8C-%EB%90%9C%EB%8B%A4%EB%A9%B4-Chain-of-Thought-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Zero-shot CoT: Large Language Models Are Zero-shot Reasoners|https://arxiv.org/abs/2205.11916|https://cartinoe5930.tistory.com/entry/Large-Language-Models-are-Zero-Shot-Reasoners-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Language Models are Multilingual Chain-of-Thought Reasoners|https://arxiv.org/abs/2210.03057|Will be uploaded later!|
|Auto-CoT: Automatic Chain of Thought Prompting in Large Language Models|https://arxiv.org/abs/2210.03493|Will be uploaded later!|
|CoT KD: Teaching Small Language Models to Reason|https://arxiv.org/abs/2212.08410|Will be uploaded later!|
|ToT: Tree of Thoughts: Deliberate Problem Solving with Large Language Models|https://arxiv.org/abs/2305.10601|https://cartinoe5930.tistory.com/entry/Tree-of-Thoughts-Deliberate-Problem-Solving-with-Large-Language-Models-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning|https://arxiv.org/abs/2305.14045|https://cartinoe5930.tistory.com/entry/CoT-Collection-Improving-Zero-shot-and-Few-shot-Learning-of-Language-Models-via-Chain-of-Thought-Fine-tuning-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Let's verify step-by-step|https://arxiv.org/abs/2305.20050|https://cartinoe5930.tistory.com/entry/Lets-verify-step-by-step-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Measuring Faitfulness in Chain-of-Thought Reasoning|https://arxiv.org/abs/2307.13702|Will be uploaded later!|
|SoT: Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding|https://arxiv.org/abs/2307.15337|Will be uploaded later!|
|Graph of Thoughts: Solving Elaborate Problems with Large Language Models|https://arxiv.org/abs/2308.09687|Will be uploaded later!|
|From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting|https://arxiv.org/abs/2309.04269|No plan!|
|Chain-of-Verification Resuces Hallucination in Large Language Models|https://arxiv.org/abs/2309.11495|https://www.youtube.com/watch?v=l0zFjwRegog&pp=ygUgaHR0cHM6Ly9hcnhpdi5vcmcvYWJzLzIzMDkuMTE0OTU%3D|
|Contrastive Chain-of-Thought Prompting|https://arxiv.org/abs/2311.09277|No plan!|
|Thread of Thought Unraveling Chaotic Contexts|https://arxiv.org/abs/2311.08734|No plan!|
|System 2 Attention (Is Something You Might Need Too)|https://arxiv.org/abs/2311.11829|No plan!|
|Chain of Code: Reasoning with a Language Model-Augmented Code Emulator|https://arxiv.org/abs/2312.04474|No plan!|

### Model Efficiency 🧰
|Paper Title|Paper|Paper Review|
|:---:|---|---|
|FlashAttention: Fast and Memory-Efficient Exact Attention|https://arxiv.org/abs/2205.14135|https://gordicaleksa.medium.com/eli5-flash-attention-5c44017022ad|
|Exponentially Faster Language Modeling|https://arxiv.org/abs/2311.10770|No plan!|
|LLM in a flash: Efficient Large Language Model Inference with Limited Memory|https://arxiv.org/abs/2312.11514|No plan!|

### Method 📐
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|Data Augmentations in NLP|blogs: https://neptune.ai/blog/data-augmentation-nlp, https://amitness.com/2020/05/data-augmentation-for-nlp/?fbclid=IwAR11MkccCti-2cD93RYftNPHb7Wxdj7AlZG7NNG4EhPaBkmiJkcBPtdl1eo|https://cartinoe5930.tistory.com/entry/Data-Augmentation-methods-in-NLP|
|PET: Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference|https://arxiv.org/abs/2001.07676|https://cartinoe5930.tistory.com/entry/PET-Exploiting-Cloze-Questions-for-Few-Shot-Text-Classification-and-Natural-Language-Inference-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Pathways|https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/|https://cartinoe5930.tistory.com/entry/%EB%A7%8C%EC%95%BD-%EB%AA%A8%EB%8D%B8%EC%9D%B4-%EC%97%AC%EB%9F%AC-%EA%B0%90%EA%B0%81%EC%9D%84-%EB%8A%90%EB%82%84-%EC%88%98-%EC%9E%88%EA%B2%8C-%EB%90%9C%EB%8B%A4%EB%A9%B4-Pathways-%EB%A6%AC%EB%B7%B0|
|LMSI: Large Language Models Can Self-Improve|https://arxiv.org/abs/2210.11610|https://cartinoe5930.tistory.com/entry/LMSI-Large-Language-Models-can-Self-Improve-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Self-Instruct: Aligning Language Model with Self Generated Instruction|https://arxiv.org/abs/2212.10560|https://cartinoe5930.tistory.com/entry/Self-Instruct-Aligning-Language-Model-with-Self-Generated-Instructions-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Reflexion: Language Agents with Verbal Reinforcement Learning|https://arxiv.org/abs/2303.11366|https://cartinoe5930.tistory.com/entry/Reflexion-Language-Agents-with-Verbal-Reinforcement-Learning-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Self-Refine: Iterative Refinement with Self-Feedback|https://arxiv.org/abs/2303.17651|https://cartinoe5930.tistory.com/entry/Self-Refine-Iterative-Refinement-with-Self-Feedback-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|REFINER: Reasoning Feedback on Intermediate Representations|https://arxiv.org/abs/2304.01904|No plan!|
|SelFee: Iterative Self-Revising LLM Expowered by Self-Feedback Generation|https://kaistai.github.io/SelFee/|https://cartinoe5930.tistory.com/entry/SelFee-Iterative-Self-Revising-LLM-Expowered-by-Self-Feedback-Generation-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints|https://arxiv.org/abs/2305.13245|https://aliissa99.medium.com/-a596e4d86f79|
|Shpherd: A Critic for Language Model Generation|https://arxiv.org/abs/2308.04592|Will be uploaded later!|
|Self-Alignment with Instruction Backtranslation|https://arxiv.org/pdf/2308.06259|Will be uploaded later!|
|SCREWS: A Modular Framework for Reasoning with Revisions|https://arxiv.org/pdf/2309.13075|No plan!|
|NEFTune: Noisy Embeddings Improve Instruction Fineuning|https://arxiv.org/abs/2310.05914|https://cartinoe5930.tistory.com/entry/Noise-makes-LLM-better-NEFTune-%F0%9F%98%89|
|Language Models are Super Mario; Absorbing Abilities from Homologous Models as a Free Lunch|https://arxiv.org/abs/2311.03099|No plan!|
|LoRAMoE: Revolutionizing Mixture of Experts for Maintaining World Knowledge in Language Model Alignment|https://arxiv.org/abs/2312.09979|No plan!|

### Retrieval Augmented Generation(RAG) 📚
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks|https://arxiv.org/abs/2005.11401|No plan!|
|Self-RAG: Learning to Retrieve, Generate, And Critique Through Self-Reflection|https://arxiv.org/abs/2310.11511|No plan!|
|InstructRetro: Instruction Tuning Post Retrieval-Augmented Pretraining|https://arxiv.org/abs/2310.07713|No plan!|
|Retrieval-Augmented Generation for Large Language Models: A Survey|https://arxiv.org/abs/2312.10997|No plan!|

### Benchmarks 🏆 & Evaluation Metric ⚔️
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|BIG-Bench Hard: Challenging BIG-Bench tasks and whether chain-of-thought can solve tham|https://arxiv.org/abs/2210.09261|Will be uploaded later!|
|Large Language Models are not Fair Evaluators|https://arxiv.org/abs/2305.17926|Will be uploaded later!|
|MT-Bench: Judging LLM-as-a-judge with MT-Bench|https://arxiv.org/abs/2306.05685|Will be uploaded later!|
|InstructEval: Towards Holistic Evaluation of Instruction-Tuned Large Language Models|https://arxiv.org/abs/2306.04757|Will be uploaded later!|
|FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets|https://arxiv.org/abs/2307.10928|Will be uploaded later!|
|GAIA: A Benchmark for General AI Assistants|https://arxiv.org/abs/2311.12983|No plan!|

### Context of LLM 📜
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|A Length-Extrapolatable Transformer|https://arxiv.org/abs/2212.10554|No plan!|
|Extending Context Window of Large Language Models via Positional Interpolation|https://arxiv.org/abs/2306.15595|https://cartinoe5930.tistory.com/entry/LM%EC%9D%98-context-window-%EA%B8%B8%EC%96%B4%EC%95%BC-%ED%95%A0%EA%B9%8C-%EC%A7%A7%EC%95%84%EC%95%BC-%ED%95%A0%EA%B9%8C-%F0%9F%93%8F%F0%9F%A4%A8|
|LongNet: Scaling Transformers to 1,000,000,000 Tokens|https://arxiv.org/abs/2307.02486|https://cartinoe5930.tistory.com/entry/LM%EC%9D%98-context-window-%EA%B8%B8%EC%96%B4%EC%95%BC-%ED%95%A0%EA%B9%8C-%EC%A7%A7%EC%95%84%EC%95%BC-%ED%95%A0%EA%B9%8C-%F0%9F%93%8F%F0%9F%A4%A8|
|Lost in the Middle: How Language Models Use Long Contexts|https://arxiv.org/abs/2307.03172|https://cartinoe5930.tistory.com/entry/LM%EC%9D%98-context-window-%EA%B8%B8%EC%96%B4%EC%95%BC-%ED%95%A0%EA%B9%8C-%EC%A7%A7%EC%95%84%EC%95%BC-%ED%95%A0%EA%B9%8C-%F0%9F%93%8F%F0%9F%A4%A8|
|YaRN: Efficient Context Window Extension of Large Language Models|https://arxiv.org/abs/2309.00071|No plan!|

### Analysis🔬
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|Why can GPT learn in-context?|https://arxiv.org/abs/2212.10559|https://cartinoe5930.tistory.com/entry/Why-can-GPT-learn-in-context-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Sparks of Artificial General Intelligence: Early experiments with GPT-4|paper: https://arxiv.org/abs/2303.12712, youtube: https://www.youtube.com/watch?v=Mqg3aTGNxZ0|https://cartinoe5930.tistory.com/entry/Sparks-of-Artificial-General-Intelligence-Early-experiments-with-GPT-4-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|The False Promise of Imitating Proprietary LLMs|https://arxiv.org/abs/2305.15717|https://cartinoe5930.tistory.com/entry/%EA%B8%B0%EC%A1%B4-imitation-model%EC%9D%80-%EC%9E%98%EB%AA%BB-%ED%95%99%EC%8A%B5%EB%90%98%EA%B3%A0-%EC%9E%88%EB%8B%A4-%F0%9F%AB%A2-The-False-Promise-of-Imitating-Proprietary-L|
|TULU: How Far Can Camels Go? Exploring the State of Instructiopn Tuning on Open Resources|https://arxiv.org/abs/2306.04751|Will be uploaded later!|
|How Is ChatGPT's Behavior Changing over Time?|https://arxiv.org/abs/2307.09009|https://cartinoe5930.tistory.com/entry/ChatGPT%EC%9D%98-%EC%84%B1%EB%8A%A5%EC%9D%B4-%EC%95%88-%EC%A2%8B%EC%95%84%EC%A7%80%EA%B3%A0-%EC%9E%88%EB%8B%A4%EA%B5%AC-%F0%9F%98%B2%F0%9F%98%B2|
|Large Language Models Cannot Self-Correct Reasoning Yet|https://arxiv.org/abs/2310.01798||No plan!|
|How Far Are Large Language Models from Agents with Theory-of-Mind|https://arxiv.org/pdf/2310.03051|No plan!|
|Can LLMs Follow Simple Rules|https://arxiv.org/abs/2311.04235|https://www.youtube.com/watch?v=CY6o43037OY|
|Camels in a Changing Climate; Enhancing LM Adaptation with Tulu 2|https://arxiv.org/abs/2311.10702|No plan!|
|ChatGPT's One-year Anniversary; Are Open-Source Large Language Models Catching up|https://arxiv.org/abs/2311.15653|No plan!|
|An In-depth Look at Gemini's Language Abilities|https://arxiv.org/abs/2312.11444|No plan!|

### Interesting🫣
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature|https://arxiv.org/abs/2301.11305|https://cartinoe5930.tistory.com/entry/%EC%9D%B4-%EA%B8%80%EC%9D%B4-LM%EC%9D%B4-%EB%A7%8C%EB%93%A4%EC%96%B4%EB%82%B8-%EA%B8%80%EC%9D%BC%EA%B9%8C-%EB%8F%84%EC%99%80%EC%A4%98-DetectGPT-DetectGPT-Zero-Shot-Machine-Generated-Text-Detection-using-Probability-Curvature-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback|https://arxiv.org/abs/2302.12813|https://cartinoe5930.tistory.com/entry/ChatGPT%EC%9D%98-hallucination-%EC%96%B4%EB%96%BB%EA%B2%8C-%ED%95%B4%EA%B2%B0%ED%95%B4%EC%95%BC-%ED%95%A0%EA%B9%8C-Check-Your-Facts-and-Try-Again-Improving-Large-Language-Models-with-External-Knowledge-and-Automated-Feedback|
|RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text|https://arxiv.org/abs/2305.13304|https://cartinoe5930.tistory.com/entry/ChatGPT%EC%97%90-%EB%B0%98%EB%B3%B5-%EB%A9%94%EC%BB%A4%EB%8B%88%EC%A6%98LSTM%EC%9D%84-%EC%82%AC%EC%9A%A9%ED%95%9C%EB%8B%A4%EB%A9%B4-RecurrentGPT-Interactive-Generation-of-Arbitrarily-Long-Text-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Large Language Models as Tool Makers|https://arxiv.org/abs/2305.17126|https://cartinoe5930.tistory.com/entry/LM%EC%9D%B4-%EB%8F%84%EA%B5%AC%EB%A5%BC-%EC%82%AC%EC%9A%A9%ED%95%98%EA%B2%8C-%EB%90%9C%EB%8B%A4%EB%A9%B4-%F0%9F%94%AC-Large-Language-Models-as-Tool-Makers-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion|https://arxiv.org/abs/2306.02561|No plan!|
|Knowledge Distillation of Large Language Models|https://arxiv.org/abs/2306.08543|https://cartinoe5930.tistory.com/entry/KD%EC%97%90-%EC%82%B4%EC%A7%9D%EC%9D%98-%EB%B3%80%ED%99%94%EB%A5%BC-%EC%A4%98%EB%B3%B4%EC%9E%90-%F0%9F%98%9C-Knowledge-Distillation-of-Large-Language-Models-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Scaling Relationship on Learning Mathematical Reasoning with Large Language Models|https://arxiv.org/abs/2308.01825|Will be uploaded later!|
|ToolLLM: Facilitating Lare Language Models to Master 16000+ Real-World APIs|https://arxiv.org/abs/2307.16789|Will be uploaded later!|
|SelfCheck: Using LLMs to Zero-shot Check Their Own Step-by-Step Reasoning|https://arxiv.org/abs/2308.00436|Will be uploaded later!|
|Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification|https://arxiv.org/abs/2308.07921|Will be uploaded later!|
|Large Language Models as Optimizers|https://arxiv.org/abs/2309.03409|No plan!|
|FIAT: Fusing Learning Paradigms with Instruction-Accelerated Tuning|https://arxiv.org/abs/2309.04663|https://www.youtube.com/watch?v=EZsZEcRDte0&pp=ygUgaHR0cHM6Ly9hcnhpdi5vcmcvYWJzLzIzMDkuMDQ2NjM%3D|
|Contrastive Decoding Improves Reasoning in Large Language Models|https://arxiv.org/abs/2309.09117|https://www.youtube.com/watch?v=nMR56TkwC1Q&pp=ygUgaHR0cHM6Ly9hcnhpdi5vcmcvYWJzLzIzMDkuMDkxMTc%3D|
|Think before you speak: Training Language Models with Pause Tokens|https://arxiv.org/abs/2310.02226|https://www.youtube.com/watch?v=MtJ1jacr_yI|
|Large Language Models Can Learn Rules|https://arxiv.org/abs/2310.07064|No plan!|
|In-context Pretraining: Language Modeling Beyond Document Boundaries|https://arxiv.org/abs/2310.10638|https://www.youtube.com/watch?v=GI-0lAaILrU|
|Learning From Mistakes Makes LLM Better Reasoner|https://arxiv.org/abs/2310.20689|No plan!|
|Language Models can be Logical Solvers|https://arxiv.org/abs/2311.06158|No plan!|
|MART: Improving LLM Safety with Multi-round Automatic Red-Teaming|https://arxiv.org/abs/2311.07689|No plan!|
|Fine-tuning Language Models for Factuality|https://arxiv.org/abs/2311.08401|No plan!|
|Positional Description Matters for Transformers Arithmetic|https://arxiv.org/abs/2311.14737|No plan!|
|Weak-to-Strong Generalization: Eliciting Strong Capabilities with Weak Supervision|https://arxiv.org/abs/2312.09390|https://openai.com/research/weak-to-strong-generalization|
|TinyGSM: achieving higher than 80 percentage on GSM8k with small language models|https://arxiv.org/abs/2312.09241|No plan!|

### Korean LM 🇰🇷
|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|Morpheme-aware Subword Tokenizer: An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks|https://arxiv.org/abs/2010.02534|Will be uploaded later!|
|What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers|https://arxiv.org/abs/2109.04650|Will be uploaded later!|

## Computer Vision

|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|history of CNN|LeNet, AlexNet, VGGNet, GoogLeNet, ResNet, ResNeXt, Sception, Mobilenet, DenseNet, EfficientNet, ConvNext|https://cartinoe5930.tistory.com/entry/CNN-network%EC%9D%98-%EC%97%AD%EC%82%AC|
|ViT: An Image Worth 16 x 16 Words: Transformers for Image Recognition at Scale|https://arxiv.org/abs/2010.11929|https://cartinoe5930.tistory.com/entry/ViT-An-Image-Worth-16-x-16-Words-Transformers-for-Image-Recognition-at-Scale|
|Swin Transformer: Hierarchical Vision Transformer using Shifted Winodws|https://arxiv.org/abs/2103.14030|https://cartinoe5930.tistory.com/entry/Swin-Transformer-Hierarchical-Vision-Transformer-using-Shifted-Windows-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|CLIP: Learning Transferable Visual Models From Natural Language Supervision|https://arxiv.org/abs/2103.00020|https://cartinoe5930.tistory.com/entry/CLIP-Learning-Transferable-Visual-Models-From-Natural-Language-Supervision-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|

## Multi-modal Models

|Paper Title|Paper or reference site Link|Paper Review|
|:---:|---|---|
|Let's learn about VLM(Visual-Language Model)|https://huggingface.co/blog/vision_language_pretraining#supporting-vision-language-models-in-%F0%9F%A4%97-transformers|https://cartinoe5930.tistory.com/entry/VLMVision-Language-Model%EC%97%90-%EB%8C%80%ED%95%B4-%EC%95%8C%EC%95%84%EB%B3%B4%EC%9E%90|
|VisualBERT: A simple and Performant Baseline for Vision and Language |https://arxiv.org/abs/1908.03557|https://cartinoe5930.tistory.com/entry/VisualBERT-A-Simple-and-Performant-Baseline-for-Vision-and-Language-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|ViLBERT: Pre-training Task-Agnostic Visiolinguistic Representations for Visual-and-Language Tasks|https://arxiv.org/abs/1908.02265|https://cartinoe5930.tistory.com/entry/ViLBERT-Pretraining-Task-Agnostic-Visiolinguistic-Representations-for-Visual-and-Language-Tasks|
|LXMERT: Learning Cross-Modality Encoder Representations from Transformers|https://arxiv.org/abs/1908.07490|https://cartinoe5930.tistory.com/entry/LXMERT-Learning-Cross-Modality-Encoder-Representations-from-Transformers-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|VL-BERT: Pre-training of Generic Visual-Linguistic Representations|https://arxiv.org/abs/1908.08530|https://cartinoe5930.tistory.com/entry/VL-BERT-Pre-training-of-Generic-Visual-Linguistic-Representations-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|VLP: Unified Vision-Language Pre-Training for Image Captioning and VQA|https://arxiv.org/abs/1909.11059|https://cartinoe5930.tistory.com/entry/VLP-Unified-Vision-Language-Pre-Traning-for-Image-Captioning-and-VQA-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks|https://arxiv.org/abs/2004.06165|https://cartinoe5930.tistory.com/entry/Oscar-Object-Semantics-Aligned-Pre-training-for-Vision-Language-Tasks-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|VinVL: Revisiting Visual Representations in Vision-Language Models|https://arxiv.org/abs/2101.00529|https://cartinoe5930.tistory.com/entry/VinVL-Revisiting-Visual-Representations-in-Vision-Language-Models-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision|https://arxiv.org/abs/2102.03334|https://cartinoe5930.tistory.com/entry/ViLT-Vision-and-Language-Transformer-Without-Convolution-or-Region-Supervision-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision|https://arxiv.org/abs/2102.05918|https://cartinoe5930.tistory.com/entry/ALIGN-Scaling-up-Visual-and-Vision-Language-Representation-with-Noisy-Text-Supervision-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|ALBEF: Vision and Language Representation Learning with Momentum Distillation|https://arxiv.org/abs/2107.07651|https://cartinoe5930.tistory.com/entry/ALBEF-Vision-and-Language-Representation-Learning-with-Momentum-Distillation-%EB%85%BC%EB%AC%B8|
|SimVLM: Simple Visual Language Model Pretraining with Weak Supervision|https://arxiv.org/abs/2108.10904|https://cartinoe5930.tistory.com/entry/SimVLM-Simple-Visual-Language-Model-Pre-training-with-Weak-Supervision-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|VLMo: Unified Vision-Language Pre-training with Mixture-of-Modality-Experts|https://arxiv.org/abs/2111.02358|https://cartinoe5930.tistory.com/entry/VLMo-Unified-Vision-Language-Pre-training-with-Mixture-of-Modality-Experts-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|LiT🔥 : Zero-Shot Transfer with Locked-image text Tuning|https://arxiv.org/abs/2111.07991|https://cartinoe5930.tistory.com/entry/LiT%F0%9F%94%A5-Zero-Shot-Transfer-with-Locked-image-text-Tuning-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|FLAVA: A Foundational Language And Vision Alignment Model|https://arxiv.org/abs/2112.04482|https://cartinoe5930.tistory.com/entry/FLAVA-A-Foundational-Language-And-Vision-Alignment-Model-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation|https://arxiv.org/abs/2201.12086|https://cartinoe5930.tistory.com/entry/BLIP-Bootstrapping-Language-Image-Pre-training-fro-Unified-Vision-Language-Understanding-and-Generation-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|

## Deep Learning Concept

|Paper or Posting Title|reference site Link|Review|
|:---:|---|---|
|Knowledge Distillation: Distilling the Knowledge in a Neural Network|https://arxiv.org/abs/1503.02531|https://cartinoe5930.tistory.com/entry/Distilling-the-Knowledge-in-a-Neural-Network-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0|
|What is Zero-shot, One-shot, Few-shot Learning?|see my blog!|https://cartinoe5930.tistory.com/entry/Zero-shot-One-shot-Few-shot-Learning%EC%9D%B4-%EB%AC%B4%EC%97%87%EC%9D%BC%EA%B9%8C|

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