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NLP，简历，深度学习纸阅读表

我读了这些与NLP和深度学习有关的论文。这是从基本到高级的各种论文。 ？此外，您可以通过单击附带的链接来检查我的韩国纸质评论。

您可以在我的博客中查看更多纸质评论，代码实施和数学描述< - 单击此处

我的见识？

我写了几篇文章，以详细解释一些深度学习技术。这些文章可以在下表中找到。

标题博客链接
NLP中的扩展法是如何制定的？ ？ https://cartinoe5930.tistory.com/entry/how-has-scaling law-developed-in-nlp-%f0%9f%A4%94-nlp%EC%97%90%90%EC%84 ％9C级别LAW％EB％EB％8A％94-％EC％96％B4％EB％96％BB％EA％B2％B2％B2％8C％EB％B0％B0％9C％EC％A0％A0％84％EB％90％90％EC％98％EC％97％88％88％EC％84％EC％84％EA 84％EA B9％B9％B9％8C
封闭源？开源??那是什么？ ？ 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％9D％ED％9D％AC％AC％EB％A7％9D-source-source-％F0％F0％9F％9F％a4％97
LM的上下文窗口，应该很长吗？应该很短吗？ ？ https://cartinoe5930.tistory.com/entry/lm%Ec%9d%98-Context-window-%B8%B8%EC%96%B4%EC%95%B C-％ED％95％A0％EA％B9％8C％EC％A7％A7％A7％EC％95％84％EC％95％BC-％ED％95％A0％EA％EA％B9％B9％8C-％F0％9F％93％93％8F％8F％8F％F0％F0％9F％9F％A4％A4％A8
评估LM的最佳方法是什么？ ？ https://cartinoe5930.tistory.com/entry/lm%Ec%9d%84-%B0%B0%80%EC； 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
Chatgpt的表现越来越糟？！？！？ ？ https://cartinoe5930.tistory.com/entry/Chatgpt%EC%9D%98-%EC%84%B1%EBV%8A%A5%EC; 88-％EC％A2％8B％EC％95％84％EC％A7％80％EA％B3％A0-％EC％9E％88％EB％EB％8B％A4％EA4％EA B5％AC-％AC-％F0％9F％95％98％B2％B2％F0％F0％F0％9F％98％98％B2
您也可以微调！用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％8B％88％EB％8B％A4-With-with-with-with-peft-％F0％9F％a4％97
让我们像人类一样逐步思考！ ？ https://cartinoe5930.tistory.com/entry/%95%95%9C-; ％EC％9D％B8％EA％B0％84％EC％B2％98％EB％9F％BC-％EC％83％83％9D％EA％B0％B0％ED％ED％95％B4％EB％B3％B3％B4％EC％EC％9E％90-％90-％90-％F0％F0％a7％a7％a7％a7％a0％a0％a0％f0％9F％a4 f0％a4 f0％a4％a4％94
微调方法的开发过程！从微调到RLHF？➡️？ https://cartinoe5930.tistory.com/entry/fine-tuning-method%ec%9d%98-%98-%EC%A7%84%ED%99%99%94-%B3%B3%BC%A0%95--
是时候微调chatgpt了！！ ⏰ https://cartinoe5930.tistory.com/entry/ %C%9D%B4%EC%A0%9c%9c%EB%8A%94-chatgpt%EB%A5%BC--fine-tuning-tuning-duning-duning%95%%A0-A0-下
噪音使LLM更好！ - 尼古纳（Neftune） https://cartinoe5930.tistory.com/entry/noise-makes-llm-better-neftune-%F0%9F%98%89

自然语言处理

单词嵌入和神经网络

纸张标题纸或参考网站链接纸质评论
嵌入矩阵https://wikidocs.net/book/2155 https://cartinoe5930.tistory.com/entry/embedding-matrix-%Ed%95%99%Ecc%8A%B5
LSTM：长期术语内存https://colah.github.io/posts/2015-08-ENDERSDANDING-LSTMS/ https://cartinoe5930.tistory.com/entry/%EC%95%8C%B8%B8%B0-%B0-%89%BD%BD%B2%B2%8C-LSTM-NETWORKS-NETWORKS-NETWORKS-NETWORKS-NETWORKS-NETWORKS-NETWORKS-NETWORKS-NETWORKS-b4%B4%B4%B4%B4%95%B4%B4；
GRU：使用rnn编码器解码器进行统计机器翻译的学习短语表示形式https://arxiv.org/abs/1406.1078 https://cartinoe5930.tistory.com/Entry/gru-empirical-evaluation-gated-gated-gated-recurrent-neurent-neurent-networks-on-sequence-modeling-modeling-modeling-modeling-vis-vis-vis-modeling-vis-modeling-vis-modeling-vis-modeling-vis-bc%Bc%BC%Ac 200-
LSTM与GRU：序列建模的封闭式复发神经网络的经验评估https://arxiv.org/abs/1412.3555 https://cartinoe5930.tistory.com/entry/lstm-vs-gru-%Eb%Ad%AD%90%90%B0%B0%B0%80-%EB%EB%8D%94-%EB%82%98%98%EC98%ECD%9D%84%84%B9%B9%8C- emp-emp-emp-emp irical评估门控的神经网络 - 序列模型％EB％EB％85％BC％EB％aC％AC％B8-％EB％A6％AC％AC％EB％b7％B7 B7％B0

语言模型？

基本的

纸张标题纸或参考网站链接纸质评论
变形金刚：您需要注意的全部https://arxiv.org/abs/1706.03762 https://cartinoe5930.tistory.com/entry/transformer-cttern-iis-is-ally-you-need-deed-vis-%85%BC%BC%EB%ACB8-%EB%A6%AC%AC%EB%B7%B0%B0；
Elmo：深层上下文化的单词表示https://arxiv.org/abs/1802.05365 https://cartinoe5930.tistory.com/entry/pre-trained-language-modeling-paper-reading-1--------------------------------------------------------------
BERT：深层双向变压器的预训练以了解语言理解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：通过生成预培训来提高语言理解https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervise/language_understanding_paper.pdf https://cartinoe5930.tistory.com/entry/pre-tration-language-modeling-paper-reading-paper-reading3-gpt-1-----------------------------------------------------------
GPT-2：语言模型是无监督的多任务学习者https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_models_are_unsupervise_multitask_learners.pdf https://cartinoe5930.tistory.com/entry/gpt-2-language-models-models-are-unsuperist-multitask-learners-learners-pinder-learners-pirners-pimbc%BC%EB%AC%B8-%EB； eb%B8-%Eb%A6%AC%EB%EB%B7%B7%B7%B7%B7%B7%B7%B7%B7%B7%B7%B7%B7%B7%B0；
GPT-3：语言模型很少https://cartinoe5930.tistory.com/entry/gpt-3-language-models-are-few-shot-learners-learners-learners-pirners-pimeb%85%BC%EB%ACB8-%B8-%EB%A6%AC%EB%EB%B7%B0%B0； https://cartinoe5930.tistory.com/entry/gpt-3-language-models-are-few-shot-learners-learners-learners-pirners-pimeb%85%BC%EB%ACB8-%B8-%EB%A6%AC%EB%EB%B7%B0%B0；
变压器-XL：超出固定长度上下文的细心语言模型https://arxiv.org/abs/1901.02860 https://cartinoe5930.tistory.com/entry/transformer-xl-cantentive-language-models-models-beyond-a- a- fixed-length-context-context-context-eb%85%BC%BC%EB%Ac%AC%AC%B8-%EB%EB%A6%ACAc%EBX%B7B7%B7％b7;
稀疏变压器：用稀疏变压器生成长序列https://arxiv.org/abs/1904.10509 https://cartinoe5930.tistory.com/entry/sparse-transformers-generating-long-sequence-with-sparse-transformers-parse-transformers-parse-transformers-dransformers-dransformers-dransformers-eb%85%BC%BC%EB;
XLNET：通用自动回报预处理以了解语言理解https://arxiv.org/abs/1906.08237 https://cartinoe5930.tistory.com/entry/xlnet-generalized-autoregrelisized-pretraining-for-langor-language-undercanding-undercanding-mertanding-vis-dranding-dranding-bc%Bc%BC%EB%Ac%B8-%EB %B8-%eb%A6%Aac 200b%EB%B7%B7%B0%B0;
Spanbert：通过表示和预测跨度改善预训练https://arxiv.org/abs/1907.10529 https://cartinoe5930.tistory.com/entry/spanbert-improving-pre-training-by-representing-and-predicting-spans-presting-spans-pans-pans-drain-s-eb%85%BC%BC%EB%ACB8-%EB8-%EB%EB%A6%ACAC%AC%EB%B7%B7%B7%B0;
罗伯塔（Roberta）：一种强大优化的BERT预训练方法https://arxiv.org/abs/1907.11692 https://cartinoe5930.tistory.com/entry/Roberta-a-Robustly-optimived-bert-pretraining-pretraining-pretraining-apphack-prace-%EB%85%BC%EB%AC%B8-%EB8-%EB；
句子 - 伯特：使用Siamese Bert-Networks的句子嵌入https://arxiv.org/abs/1908.10084 https://cartinoe5930.tistory.com/entry/sentence-bert-sentence-sentence-embeddings-using-siamese-bert-networks-bert-networks-mertworks-drworks-drworks-viss-bc%BC%EB%AC%B8-%Eb%A6%AC%AC%EB%EB%B7%B7%B0%B0
阿尔伯特：一个用于自我监督语言表征学习的精简版https://arxiv.org/abs/1909.11942 https://cartinoe5930.tistory.com/entry/albert-a-lite-lite-for-self-supersef-supersef-language-presentations-langue-presentations-presentations-prensentations-piresentations-pimeb%85%BC%BC%EB%AC%B8-%EB8-%EB%EB%A6%ACAC%EB%B7B7%B7%B7;
巴特：自然语言生成，翻译和理解的序列前训练序列前训练https://arxiv.org/abs/1910.13461 https://cartinoe5930.tistory.com/entry/bart-denoising-sequence-sequence-toence-toence-pre-pre-training-for-natural-natural-natural-language-generation-generation-generation-generation-ranslation-and-comprehention-and-comprehens-d-comprehens-bcomeb%85%Bc%BC%EB%B%Acac，B8-
前LN变压器：在变压器体系结构中的图层归一化https://arxiv.org/abs/2002.04745 https://cartinoe5930.tistory.com/entry/pre-ln-transformer-on-layer-normalization-in-the-the-transformer-architecture-marchitutecor-br%85%BC%EB%AC%B8-%EB8-%EB%A6%ACAC 200B%EB%B7%B7%B7%B0；
Electra：训练前文本编码作为歧视者而不是发电机https://arxiv.org/abs/2003.10555 https://cartinoe5930.tistory.com/entry/Etectra-pre-training-training-training-text-encoders-ans-as-discriminators-rather-than-Generators
longformer：长篇文档变压器https://arxiv.org/abs/2004.05150 https://cartinoe5930.tistory.com/entry/longformer-the-long-document-document-transformer-dransformer-%EB%85%BC%EB%ACACB8-%EB%A6%AC%EB%B7%B0；
Bigbird：更长序列的变压器https://arxiv.org/abs/2007.14062 https://cartinoe5930.tistory.com/entry/bigbird-transformers-for-longer-sequences-pinces-dimences-vis-vis-vis-vis-vis-bc%eb%Acac%B8-%EB%A6%Ac%EB%B7%B7%B0；
WebGPT：通过人类反馈的浏览器协助提问https://arxiv.org/abs/2112.09332 https://cartinoe5930.tistory.com/entry/webgpt-browser-assisted-question-question-withering-with-human-feedback-debback-%eb%85%Bc%BC%EB%AC%B8-%EB8-%EB %B%EB%A6%ACAC%EAC：EB%EB%B7%B7%B0%B0；
选择：开放预训练的变压器语言模型https://arxiv.org/abs/2205.01068 https://cartinoe5930.tistory.com/entry/opt-open-pre-trained-tration-transformer-language-models-models-models-models-%EB%85%BC%EB%Acac%b8-%EB8-%EB%A6%AC%AC%EB%EB%B7%B0%B0
Mamba：具有选择性状态空间的线性时间序列建模https://arxiv.org/abs/2312.00752没有计划！

高效的模型？

纸张标题纸或参考网站链接纸质评论
Tinybert：将Bert提炼为自然语言理解https://arxiv.org/abs/1909.10351 https://cartinoe5930.tistory.com/entry/tinybert-distilling--bert-for-natural-natural-language-underage-undercanding-undercanding-dermestand-distand- %B%85%BC%EB%AC：B8-%B8-%Eb%A6%ACMAACAC%AC%AC%EB%B,B7%B0；
Distilbert：Bert的蒸馏版https://arxiv.org/abs/1910.01108 https://cartinoe5930.tistory.com/entry/distilbert-a-distille-version-of-bert-smaller-faster-faster-cheaper-cheaper-cheaper-cheaper-and-light---- and-light-%EB%85%BC%BC%EB%AC%B8-%eb%EB%A6%A6%AC：2%AC%EB%B7%B7%B7%B0;
重要的不仅很重要：小语言模型也很少是学习者（PET응용） https://arxiv.org/abs/2009.07118 https://cartinoe5930.tistory.com/entry/its-not-just-size-that-matter-mall-language-models-models-models-are-also-few-shot-shot-learners--phist-learners--eb%BC%85%BC%EB%EB%ACACB8-V8-

开源语言模型（缩放定律）？

纸张标题纸或参考网站链接纸质评论
Chinchilla：培训的计算最佳大语模型https://arxiv.org/abs/2203.15556 https://cartinoe5930.tistory.com/entry/%EC%A7%80%80%B8%88-%88-%B9%8C%EC%A7%80%E C％9D％98-LM-SCALING LAW％EC％97％97％90％EB％8A％94-％EB％AC％AC％EC％EC％A0％9c％EC％A0％A0％90％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
腓热：一套用于分析跨培训和扩展的大型语言模型的套件https://arxiv.org/abs/2304.01373没有计划！
利马：更少的是对齐https://arxiv.org/abs/2305.11206 https://cartinoe5930.tistory.com/entry/lima-sir-is-is-more-for-alignment-alignment-prim;
骆驼：开放有效的基础语言模型https://arxiv.org/abs/2302.13971 https://cartinoe5930.tistory.com/entry/llama-open-and-foundation-foundation-language-models-models-models-%EB%85%BC%EB%ACACACB8-%EB%A6%AC%EB%EB%B7%B0；
wizardlm：授权大语言模型遵循复杂的说明https://arxiv.org/abs/2304.12244 https://cartinoe5930.tistory.com/entry/open-domain-instruction%ec%9d%98-%98-%9a%A8%B3%B3%B3%B3%BC-%9F%AA%84-WIZ ARDLM授权的大量语言模型到遵循式 - 复合构建 - ％EB％85％BC％EB％AC％AC％B8-％EB％A6％AC％AC％EB％B7％B7
WizardCoder：通过Evol-Inscruct授权代码大语言模型https://arxiv.org/abs/2306.08568 https://huggingface.co/wizardlm/wizardcoder-15b-v1.0
巫师：通过增强的Evol-Infruction赋予大语言模型的数学推理能力https://arxiv.org/abs/2308.09583 https://huggingface.co/wizardlm/wizardmath-70b-v1.0
羊驼：强大的，可复制的指导跟踪模型https://crfm.stanford.edu/2023/03/13/alpaca.html https://cartinoe5930.tistory.com/entry/alpaca-a-a-strong-replicable-rplicable-instruction-following-model-model-model-del-model-%a6%Acas%EB%B7%B0；
Vicuna：一个开源聊天机器人给GPT-4留下深刻印象https://lmsys.org/blog/2023-03-03-30-Vicuna/ https://cartinoe5930.tistory.com/entry/vicuna-an-open-source-source-chatbot-impressing-gpt-4-; gpt-4-%EB%A6%AC%EB%B7%B0；
考拉：学术研究对话模型https://bair.berkeley.edu/blog/2023/04/03/koala/ https://cartinoe5930.tistory.com/entry/%EC%A4%91%EC%9A%9A%94%94%95%9C-%9C-%B1%B1%B4-%B4-%BA%BA%BE%ECB4； ％A7％80-％EC％95％8A％EB％8A％94高质量data-koala％F0％9F％90％A8-A-Dialogue-Modegoge-Model-for-Academic-for-Academic-Arsearc
Baize：一个开源聊天模型，具有自chat数据的参数效率调整https://arxiv.org/abs/2304.01196 https://cartinoe5930.tistory.com/entry/%F0%9f%90%90%B2BAIZE-AN-OPEN-SOURCE-CHAT-CHAT-CHAT-MODEL-WITH-PARAMETER--PARAMETER-FIFICE-TUNING-TUNING-on-self-Chat-c​​hat-c​​hat-c​​hat-data-chat-data-chat-data-chat-data-%EB%8555555%BC%BC%ACB8-B8--eb%B; b;
扩展数据受限的语言模型https://arxiv.org/abs/2305.16264 https://www.youtube.com/watch?v=tk0-sitkcmw&pp=ygugahr0chm6ly9hcnhpdi5vcmcvcmcvywjzlzizmduumtyynjq%3d
Falcon＆Repinedweb https://arxiv.org/abs/2306.01116 https://cartinoe5930.tistory.com/entry/open-llm-leaderboard%EB%A5%BC- %BC-%9C%A9C%A9%EC%93%B4-FALCON%F0%9f%A6%85-lllm-falcon-falcon-refelinedweb
ORCA：从GPT-4的复杂解释痕迹中进行渐进学习https://arxiv.org/pdf/2306.02707 https://cartinoe5930.tistory.com/entry/%F0%9F%90%90%Acorca-progression-learning-from-complex-complex-explanation-traces-of-gpt-gpt-4-%EB%85%BC%BC%EB%B8-
PHI-1：您需要教科书https://arxiv.org/abs/2306.11644 https://cartinoe5930.tistory.com/entry/ %%95%84%Ec9a%9A%94%94%95%95%9C-%B1B1%B4-%B4-%EC98%A4%EC%A7; %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：培训一个更好的羊驼的数据https://arxiv.org/abs/2307.08701稍后将上传！
骆驼2：开放基础和微调聊天模型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％9D％ED％9D％AC％AC％EB％A7％9D-source-source-％F0％F0％9F％9F％a4％97
Platypus：快速，便宜且有力的LLMS https://arxiv.org/abs/2308.07317稍后将上传！
代码骆驼：代码的开放基础模型https://arxiv.org/abs/2308.12950没有计划
FLM-101B：开放LLM以及如何以10万美元的预算培训https://arxiv.org/pdf/2309.03852没有计划！
教科书就是您需要的II：PHI-1.5技术报告https://arxiv.org/abs/2309.05463 https://huggingface.co/microsoft/phi-1_5
OpenChat：使用混合质量数据推进开源语言模型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：在语言模型中诱导细粒度的评估能力https://arxiv.org/abs/2310.08491 https://huggingface.co/papers/2310.08491#652A8E7F30355BEBA68C1BE6
Zephyr：直接蒸馏LM对齐https://arxiv.org/abs/2310.16944 https://www.youtube.com/watch?v=tkzbg3mksio
ORCA2：教小语言模型如何推理https://arxiv.org/abs/2311.11045 https://www.microsoft.com/en-us/research/blog/orca-2-teaching-small-language-models-models-how-to-reason/
猎鹰系列开放语言模型https://arxiv.org/abs/2311.16867没有计划！
太阳能10.7b：用简单而有效的深度缩放大型语言模型https://arxiv.org/abs/2312.15166没有计划！

大语言模型（LLM）？

纸张标题纸或参考网站链接纸质评论
LAMDA：对话应用程序的语言模型博客：https：//ai.googleblog.com/2022/01/lamda-towards-safe-grounded-and-high.html，论文：https：//arxiv.org/abs/2201.08239 https://cartinoe5930.tistory.com/entry/%B5%AC%AC%B8%B8%80%EC9D%9D%98-%EC%B5%9C%9C%B0%95-%ECB1B1B1B1B1B1B1%97%EBB4； ％80％ED％95％B4-％EC％95％8C％EC％95％84％EB％B3％B3％B4％EC％9E％90语言模型 - dialog-dialog-applications-％eb％EB％EB％85％BC％bc％eb％eb％AC％AC％AC％AC％AC％AC％B8-％EB％AC％AC％AC％AC％AC％AC％AC％B7％B7％B7
棕榈：用途径进行缩放语言建模博客：https：//ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html，论文：https：//arxiv.org/abs/2204.022311 1： https://cartinoe5930.tistory.com/entry/lamda%Ecd%9d%98-%EB%92%92%A4%EB%A5%Bc- bc-; 99％9C％EC％9A％A9％ED％95％95％9C％EC％B4％B4％88％EA％B1％B0％B0％EB％8C％80-％EC％96％B8％EC％EC％96％B4-％B4％B4％EB％AA％AA％AA％aa a. 2： https://cartinoe5930.tistory.com/entry/lamda%Ec%9d%9d%98-%EB%92%92%A4%EB%A5%BC-C-- %B-; 82％AC％EC％9A％A9％ED％95％9C-％EC％B4％88％EA％B1％B0％B0％EB％8C％8C％80-％EC％96％B8％EC％96％B4-％B4％B4％B4％AA％aa％AA％aa aa aa aa a.8dd dd％b8 palm-％b8 palm-％b8 palm-％b8 palm％eb％eb％aC％eb％eb％eb％eb％eb％b7％b7％b7％b7％b02
GPT-4：技术评论博客：https：//openai.com/research/gpt-4，论文：https：//arxiv.org/abs/2303.08774 https://cartinoe5930.tistory.com/entry/gpt-4-techinal-report-review
双子座：一个高度强大的多模型的家族https://arxiv.org/abs/2312.11805没有计划！
字母2技术报告https://storage.googleapis.com/deepmind-media/alphacode2/alphacode2_tech_report.pdf没有计划！

微调

指导调用？‍？

纸张标题纸或参考网站链接纸质评论
Flan：微调语言模型是零摄的学习者https://arxiv.org/abs/2109.01652 https://cartinoe5930.tistory.com/entry/flan-fine-tuned-language-models-models-aare-zero-sero-shot-learners-learners-learners-pimbc%BC%EB%AC%B8-%B8-%eb%A6%AC%AC%EB%B7%B0%B0；
T0：多任务提示培训可以启用零击任务概括https://arxiv.org/abs/2110.08207 https://cartinoe5930.tistory.com/entry/t0-multitask-prompted-training-enables-zero-sero-sero-sero-sero-sero-sero-shot-task-generalization-dask-generalization-bc%85%BC%BC%EB%AC%B8-%EB8-：
超自然说明：通过1600+ NLP任务的声明说明进行概括https://arxiv.org/abs/2204.07705 https://cartinoe5930.tistory.com/entry/super-natural-instructions-generalization-gia-via-declarative-instructions-insustions-on-1600-nlp-tasks--tasks-pasks-inlp-tasks-eb%BC%85%BC%EB%EB%B；
不自然的说明：使用（几乎）不是人工劳动的语言模型https://arxiv.org/abs/2212.09689稍后将上传！
猜猜指令！翻转学习使语言模型更强https://arxiv.org/abs/2210.02969 https://cartinoe5930.tistory.com/entry/guess-the-sinstruction-flippertion-learning-makes-language-models-models-models-model-stronger-zer-stronger-stronger-stronger-strong-loshot-learners-phis-pimiseb%85%Bc%BC%EB%Ac 200-
缩放指令 - 通信语言模型https://arxiv.org/abs/2210.11416 https://cartinoe5930.tistory.com/entry/scaling-instruction-finetuned-language-models-models-models-%EB%85%BC%EB%EB%ACB8-%EB%A6%AC%EB%EB%B7%B0；
探索培训专家语言模型比教学调整的好处https://arxiv.org/abs/2302.03202 https://cartinoe5930.tistory.com/entry/Exploring-the-benefits-of-training-training-training-training-training-language-models-models-models-models-ober-instruction-tuning-tuning-duning-duning-duning-duning-duning-duning-dunis-85%Bc%Bc%Eb%Ac%B8-%EB/%EB%EB%A6%AC，eb%B7：B7：B7：B7：B7B7%B7;
ICIL：在文章中的教学学习https://arxiv.org/abs/2302.14691 https://cartinoe5930.tistory.com/entry/icil-in-in-context-instext-instruction-learning-mearning-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0%B0；
使用GPT-4进行指导调整https://arxiv.org/abs/2304.03277 https://cartinoe5930.tistory.com/entry/instruction-tuning-with-with-gpt-4-pm%EB%85%BC%BC%AC%AC%B8-%EB%A6%AC%EB%B7%B0%B0
FIP：固定输入参数化以进行有效提示https://aclanthology.org/2023.findings-acl.533.pdf稍后将上传！
Flacuna：使用Flan微调释放Vicuna的问题解决能力https://arxiv.org/abs/2307.02053稍后将上传！
也许只需要0.5％的数据：对低训练数据指导调整的初步探索https://arxiv.org/abs/2305.09246稍后将上传！
成为自我教育：引入最小的指示调整的早期停止标准https://arxiv.org/abs/2307.03692稍后将上传！

从人类反馈（RLHF）学习的强化学习

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RLHF（从人类反馈中学习） https://huggingface.co/blog/rlhf https://cartinoe5930.tistory.com/entry/%EC%82%AC%EB%9E%9E%8C%ECD%9D%98-%98-%94%BC%93%93%9C%EB%B0%B1B1B1%B1%ECM%9D%84 -％ED％86％B5％ED％95％9C-％EA B0％B0％95％ED％99％99％ED％95％99％EC％8A％B5-Forminforcement-fured-Formenting Learning-From-Human-Human-Feedback-Rlhf
用语言模型的红色小组语言模型https://arxiv.org/abs/2202.03286 https://cartinoe5930.tistory.com/entry/red-teaming-teaming-language-models-with-language-models-models-models-%EB%85%BC%EB%ACACB8-;
指示：培训语言模型遵循人类反馈的指示https://arxiv.org/abs/2203.02155 https://cartinoe5930.tistory.com/entry/instructgpt-training-training-models-models-models-to-follow-instructions-with-human-feedback-debback-%EB%85%BC%BC%EB%AC%B8-%EB8-：
通过从人类反馈中学习的强化学习培训有用且无害的助手https://arxiv.org/abs/2204.05862 https://cartinoe5930.tistory.com/entry/training-a-helpful-and-harmless-assistant--with-reinforecement-learning-from-human-feedback-feedback-feedback-d.bc%85%Bc%Bc%BC%BC%ACB8-%B8-%EB;
Alpacafarm：从人类反馈中学习的方法的模拟框架https://arxiv.org/abs/2305.14387稍后将上传！
几乎是：通过合成反馈对齐大语言模型https://arxiv.org/abs/2305.13735 https://cartinoe5930.tistory.com/entry/aligning-large-lange-lange-models-models-though-synthetic-feedback-back-back-%eb%85%BC%EB%AC%B8-%EB8-%B8-％
从人类反馈中学习的开放问题和强化学习的根本局限性https://arxiv.org/abs/2307.15217稍后将上传！
RLAIF：通过AI反馈从人类反馈中缩放增强的学习https://arxiv.org/abs/2309.00267没有计划！
steerlm：属性条件为SFT作为RLHF的（用户转速）替代品https://arxiv.org/abs/2310.05344没有计划！
HelpSteer：Steerlm的多属性帮助数据集https://arxiv.org/abs/2311.09528没有计划！

高效调整

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适配器：NLP的参数效率学习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％8B％88％EB％8B％A4-With-with-with-with-peft-％F0％9F％a4％97
前缀调整：优化发电的连续提示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％8B％88％EB％8B％A4-With-with-with-with-peft-％F0％9F％a4％97
洛拉：大语言模型的低排名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％8B％88％EB％8B％A4-With-with-with-with-peft-％F0％9F％a4％97
迈向参数效率转移学习的统一观点https://arxiv.org/abs/2110.04366稍后将上传！
Unipelt：参数有效语言模型调整的统一框架https://arxiv.org/abs/2110.07577稍后将上传！
（ia）^3：几乎没有参数效率的微调比在文化学习中更好，更便宜https://arxiv.org/abs/2205.05638稍后将上传！
QLORA：量化LLM的有效微调https://arxiv.org/abs/2305.14314稍后将上传！
堆叠更多的层不同：通过低排名更新的高级培训https://arxiv.org/abs/2307.05695稍后将上传！
Lorahub：通过动态洛拉组成有效的跨任务概括https://arxiv.org/abs/2307.13269稍后将上传！

数据集？

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指令挖掘：大语言模型的高质量指令数据选择https://arxiv.org/abs/2307.06290没有计划！
苏打水：社交常识性上下文化的百万级对话蒸馏https://arxiv.org/abs/2212.10465没有计划！
mod：指导调整的面向模型的数据选择https://arxiv.org/abs/2311.15653没有计划！
超越人类数据：通过语言模型来扩展解决问题的自我训练https://arxiv.org/abs/2312.06585没有计划！
MagicOder：源代码就是您所需要的https://arxiv.org/abs/2312.02120没有计划！
WaveCoder：通过精制数据生成的广泛和多功能增强的指令调整https://arxiv.org/abs/2312.14187没有计划！
是什么使良好的数据保持对齐方式：对教学调整中自动数据选择的全面研究https://arxiv.org/abs/2312.15685没有计划！

及时工程？‍？

纸张标题纸或参考网站链接纸质评论
什么是“及时工程”？查看我的博客！ https://cartinoe5930.tistory.com/entry/prompt-eendering%EC%9D%B4-%EB%AC%B4%EC%97%87%ECMEC9D%BC%BC%BC%B9%8C
COT：经过深思熟虑链的促使大语言模型引起推理博客：https：//ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html，论文：https：//arxiv.org/abs/2201.11903 https://cartinoe5930.tistory.com/entry/lm%Ec%9D%B4-%EC%82%AC%AC%EB%9E%9E%9E%8C%B3%B3%BC-%BC-%9C%9C%A0%EC%82%82%82; %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
零镜头婴儿床：大型语言模型是零击的推理器https://arxiv.org/abs/2205.11916 https://cartinoe5930.tistory.com/entry/large-lange-models-models-are-zero-sero-sero--reasoners-jbc%85%BC%EB%ACB8-%EB%A6%AC%AC%EB%BX%B7%B0%B0；
语言模型是多语言链的推理者https://arxiv.org/abs/2210.03057稍后将上传！
自动cot：自动思想链在大型语言模型中提示https://arxiv.org/abs/2210.03493稍后将上传！
COT KD：教小语言模型以推理https://arxiv.org/abs/2212.08410稍后将上传！
TOT：思想树：大型语言模型的故意解决问题https://arxiv.org/abs/2305.10601 https://cartinoe5930.tistory.com/entry/tree/tree-of-thoughts-deliberate-problem-solving-with-large-lange-lange-lange-models-models-models-models-models-models-models-models-modelm%85%BC%BC%EB%AC%B8-%EB8-%EB%EB%A6%ACAC 200B%B7%B7%B0%B0;
COT集合：通过经过思考的微型调整改善语言模型的零拍摄和几乎没有射击的学习https://arxiv.org/abs/2305.14045 https://cartinoe5930.tistory.com/entry/cot-collection-collection-improving-zero-sero-shot-shot-and-hot-few-shot--few-shot-learning of-language-models-models-models-via-chain of-thought-thought-thought-thought-tuning-tuning-puning-puning-duning-duning-%EB%85%BC%BC%EB%EB%ACACACB8-%B8-%B8-%Ac%Ac%B-7B8-;
让我们逐步验证https://arxiv.org/abs/2305.20050 https://cartinoe5930.tistory.com/entry/lets-verify-step-by-step-dep-%Eb%85%BC%BC%AC%AC%B8-%EB%A6%AC%EB%B7%B7%B0；
衡量经济链推理中的相性https://arxiv.org/abs/2307.13702稍后将上传！
SOT：经过思考的骨骼：大语言模型可以进行平行解码https://arxiv.org/abs/2307.15337稍后将上传！
思想图：解决大型语言模型的详尽问题https://arxiv.org/abs/2308.09687稍后将上传！
从稀疏到密集：GPT-4汇总，并链链链https://arxiv.org/abs/2309.04269没有计划！
验证链在大语言模型中唤起了幻觉https://arxiv.org/abs/2309.11495 https://www.youtube.com/watch?v=l0zfjwregog&pp=ygugahr0chm6ly9hcnhpdi5vcmcvywjzlzizmdkumte0otu%3d
对比度链的促使https://arxiv.org/abs/2311.09277没有计划！
思想界线解开混乱的环境https://arxiv.org/abs/2311.08734没有计划！
系统2注意（您也可能需要的东西） https://arxiv.org/abs/2311.11829没有计划！
代码链：使用语言模型的代码模拟器推理https://arxiv.org/abs/2312.04474没有计划！

模型效率？

纸张标题纸纸质评论
闪存：快速且记忆效益的精确注意https://arxiv.org/abs/2205.14135 https://gordicaleksa.medium.com/eli5-flash-astention-5c444017022ad
指数级更快的语言建模https://arxiv.org/abs/2311.10770没有计划！
闪光灯中的LLM：有限的内存有效的大型语言模型推断https://arxiv.org/abs/2312.11514没有计划！

方法 ？

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NLP中的数据增强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-aigmentation-methods-in-nlp
宠物：利用几张镜头文本分类和自然语言推断的披肩问题https://arxiv.org/abs/2001.07676 https://cartinoe5930.tistory.com/entry/pet-exploiting-cloze-questions-for-for-for-for-few-shot-text-classification-and-natural-language-inference-inperion-inference-inperion-inperion-inperion-inperion-inperion-inperion-inperion-bc%85%Bc%BC%EB%ACB；
途径https://blog.google/technology/ai/introducing-pathways-nextways-next-generation-ai-Architection/ https://cartinoe5930.tistory.com/entry/%EB%A7%8c%95%BD-%BD-%AA； B0％81％EC％9D％84-％EB％8A％90％EB％82％82％84-％EC％88％98-％EC％9E％9E％88％EA％B2％B2％8C-％EB％90％90％9C％9C％EB％EB％EB％EB％A4％A4％EB％EB％EB％A9％A9％A9％B4-PATHWAYS-EB％B4-PATHWAREED-EB％A6％EB％a6％a6％AC％AC％AC％B7％B7％B7％B7
LMSI：大型语言模型可以自我突破https://arxiv.org/abs/2210.11610 https://cartinoe5930.tistory.com/entry/lmsi-large-lange-models-models-models-can-self-improve-%EB%85%BC%EB%ACB8-%B8-%EB%A6%AC%EB%EB%B7%B0%B0；
自我建造：将语言模型与自我生成的指示结合https://arxiv.org/abs/2212.10560 https://cartinoe5930.tistory.com/entry/self-instruct-aligning-language-model-model-with-self-generated-instructions-instructions-minstructions-dimstructions-pimbc%85%BC%EB%AC%AC%B8-%EB;
反射：语言加强学习的语言代理商https://arxiv.org/abs/2303.11366 https://cartinoe5930.tistory.com/entry/reflexion-language-agents-with-verbal-reinforcement-learning-learning-learning-learning-eb%85%BC%EB%AC%B8-%B8-%eb%A6%AC%AC%EB%EB%B7%B0%B0
自我refine：迭代精致和自我反馈https://arxiv.org/abs/2303.17651 https://cartinoe5930.tistory.com/entry/self-refine-iterative-refinement-with-self-self-feedback-debback-%EB%85%BC%EB%AC%B8-%Eb%A6%AC%AC%EB%EB%B7%B0%B0；
炼油厂：中间表示的推理反馈https://arxiv.org/abs/2304.01904没有计划！
自由：迭代自我申请LLM由自我反馈生成升级https://kaistai.github.io/selfee/ https://cartinoe5930.tistory.com/entry/selfee-iterative-self-revisis--revising-llm-expate-by-self-feedback-generation-eb%85%Bc%BC%EB%Ac%B8-%EB8-%EB%A6%ACMAC%EB%B7%B7%B7%B0%B0;
GQA：培训多头检查点的通用多电量变压器模型https://arxiv.org/abs/2305.13245 https://aliissa99.medium.com/-A596E4D86F79
Shpherd：语言模型生成的评论家https://arxiv.org/abs/2308.04592稍后将上传！
自我调整与教学反向翻译https://arxiv.org/pdf/2308.06259稍后将上传！
螺丝：通过修订的模块化框架https://arxiv.org/pdf/2309.13075没有计划！
Neftune：嘈杂的嵌入式嵌入改善教学方法https://arxiv.org/abs/2310.05914 https://cartinoe5930.tistory.com/entry/noise-makes-llm-better-neftune-%F0%9F%98%89
语言模型是超级马里奥；来自同源模型的吸收能力作为免费午餐https://arxiv.org/abs/2311.03099没有计划！
Loramoe：革新专家的混合物，以维持世界知识在语言模型时https://arxiv.org/abs/2312.09979没有计划！

检索增强发电（RAG）

纸张标题纸或参考网站链接纸质评论
用于知识密集型NLP任务的检索生成一代https://arxiv.org/abs/2005.11401没有计划！
自我剥离：学会通过自我反省来检索，产生和批评https://arxiv.org/abs/2310.11511没有计划！
仪表盘：指导调整后检索预告片https://arxiv.org/abs/2310.07713没有计划！
大型语言模型的检索演示一代：一项调查https://arxiv.org/abs/2312.10997没有计划！

基准？和评估公制⚔️

纸张标题纸或参考网站链接纸质评论
大板凳艰难：具有挑战性的大台任务以及经过思考链是否可以解决THAM https://arxiv.org/abs/2210.09261稍后将上传！
大型语言模型不是公平的评估者https://arxiv.org/abs/2305.17926稍后将上传！
MT-Bench: Judging LLM-as-a-judge with MT-Bench https://arxiv.org/abs/2306.05685稍后将上传！
讲座：朝着教学调节的大语言模型的整体评估https://arxiv.org/abs/2306.04757稍后将上传！
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

纸张标题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!

分析？

纸张标题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
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!

有趣的？

纸张标题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!
大型语言模型的知识蒸馏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 ??

纸张标题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!

计算机视觉

纸张标题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

纸张标题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审查
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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