생성형 AI 및 LLM 관련 Reading List

 

LLM (Large Language Model) Reading List

• NLP's ImageNet moment has arrived (NLP ImageNet 시대의 도래) : https://thegradient.pub/nlp-imagenet/
• Google Cloud supercharges NLP with large language models (Google Cloud 대규모 언어모델로 NLP강화) : https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-supercharges-nlp-with-large-language-models
• LaMDA: our breakthrough conversation technology (LaMDA : Google의 혁신적인 대화 기술) : https://blog.google/technology/ai/lamda/
• Language Models are Few-Shot Learners (언어 모델의 퓨샷(Few-Shot) 학습) : 
  https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64aPaper.pdf
• PaLM-E: An embodied multimodal language model (PaLM-E: 내장 멀티모달 언어 모델) :
  https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html
• Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance
  (PaLM: 획기적인 성능을 위해 5,400억 개의 매개변수로 확장) :
  https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html
• The Powerof Scale for Parameter-Efficient Prompt Tuning (매개변수 효율적 프롬프트 미세 조정의 확장) :
  https://arxiv.org/pdf/2104.08691.pdf
• Google Research, 2022 & beyond: Language, vision and generative models (2022년 이후 Google Research 언어모델) :
  https://ai.googleblog.com/2023/01/google-research-2022-beyond-language.html#LanguageModels
• Accelerating text generation with Confident Adaptive Language Modeling (CALM)
  (CALM을 통한 보다 빠른 텍스트 생성) : https://ai.googleblog.com/2022/12/accelerating-text-generation-with.html
• Solving a machine-learning mystery (머신러닝에 대한 의문점 해소):
  https://news.mit.edu/2023/large-language-models-in-context-learning-0207

 

생성형 AI (Generative AI) Reading List

• Ask a Techspert: What is generative AI? (기술 전문가에게 질문하기:생성형 AI란 무엇인가요?) :
  https://blog.google/inside-google/googlers/ask-a-techspert/what-is-generative-ai/
• Build new generative AI powered search & conversational experiences with Gen App Builder (생성형 앱 빌더를 사용한 새로운 생성형AI 기반 검색 및 대화형 환경 빌드) :
   https://cloud.google.com/blog/products/ai-machine-learning/create-generative-apps-inminutes-with-gen-app-builder
• What is generative AI? (생성형 AI란 무엇인가요?)
  https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai
• Google Research, 2022 & beyond: Language, vision and generative models (2022년 이후 Google Research 생성 모델) :
  https://ai.googleblog.com/2023/01/google-research-2022-beyond-language.html#GenerativeModels
• Building the most open and innovative AI ecosystem (가장 개방적이고 혁신적인 AI 생태계 구축) :
  https://cloud.google.com/blog/products/ai-machine-learning/building-an-open-generative-ai-partner-ecosystem
• Generative AI is here. Who Should Control It? (생성형 AI의 등장, 누가 통제할 것인가?) : 
  https://www.nytimes.com/2022/10/21/podcasts/hard-fork-generative-artificial-intelligence.html
• Stanford U & Google’s Generative Agents Produce Believable Proxies of Human Behaviors (인간의 행동에 대한 신뢰할 수 있는 프록시를 만드는 스탠포드 대학교와 Google의 생성형 에이전트) :
  https://syncedreview.com/2023/04/12/stanford-u-googles-generative-agents-produce-believable-proxies-of-human-behaviours/
• Generative AI: Perspectives from Stanford HAI (생성형 AI : Stanford HAI의 견해) :
  https://hai.stanford.edu/sites/default/files/2023-03/Generative_AI_HAI_Perspectives.pdf
• Generative AI at Work (업무에 사용되는 생성형 AI) :
  https://www.nber.org/system/files/working_papers/w31161/w31161.pdf
• Thefuture of generative AI is niche, not generalized (보편화되기보다는 전문화될 생성형 AI의 미래) :
  https://www.technologyreview.com/2023/04/27/1072102/the-future-of-generative-ai-is-niche-not-generalized/
• Theimplications of Generative AI for businesses (생성형 AI가 비즈니스에 미치는 영향) :
  https://www2.deloitte.com/us/en/pages/consulting/articles/generative-artificial-intelligence.html
• Proactive Risk Management in Generative AI (생성형 AI의 사전 예방적 위험 관리) :
  https://www2.deloitte.com/us/en/pages/consulting/articles/responsible-use-of-generative-ai.html
• How Generative AI Is Changing Creative Work (생성형 AI가 창의적인 작업을 변화시키는 방식) :
  https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work

 

기타 Reading List

• Attention is All You Need (어텐션이 필요한 전부입니다, 관심을 기울이기만 하면됩니다) :
  https://research.google/pubs/pub46201/
• Transformer : A Novel Neural Network Architecture for Language Understanding (트랜스포머: 언어 이해를 위한 새로운 신경망 아키텍처 :
  https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html
• Transformer on Wikipedia (Wikipedia의 트랜스포머) :
  https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)#:~:text=Transformers%20were%20introduced%20in%202017,allowing%20training%20on%20larger%20datasets
• What isTemperature in NLP? (NLP 강도의 의미) :
  https://lukesalamone.github.io/posts/what-is-temperature/
• Bard now helps you code (코딩을 도와주는 Bard) : https://blog.google/technology/ai/code-with-bard/
• Vertex AI의 Model Garden : https://cloud.google.com/model-garden
• Auto-generated Summaries in Google Docs (Google Docs의 자동 생성 요약) :
  https://ai.googleblog.com/2022/03/auto-generated-summaries-in-google-docs.html

 

(Source : Google Cloud)