#fewshotlearning — Public Fediverse posts

via #AIFoundry : Azure Translator: Improving Translation Quality with Adaptive Datasets and Few‑Shot Learning

https://ift.tt/JGMPZ8D
#AzureTranslator #AdaptiveDatasets #FewShotLearning #MachineTranslation #NLP #AI #Foundry #TechBlog #TranslationQuality #DomainContext #Termino…

via #AIFoundry : Azure Translator: Improving Translation Quality with Adaptive Datasets and Few‑Shot Learning

https://ift.tt/JGMPZ8D
#AzureTranslator #AdaptiveDatasets #FewShotLearning #MachineTranslation #NLP #AI #Foundry #TechBlog #TranslationQuality #DomainContext #Termino…

#TripleNet #ContinualLearning #MachineLearning #AI #NeuralNetworks #DeepLearning #CatastrophicForgetting #LifelongLearning #NeuralArchitecture #ResonantMemory #FewShotLearning #SOTA #Benchmark #SplitMNIST #Research #OpenSource #AIResearch #RussianAI #MadeInRussia

AdaMix proves its edge in few-shot NLU, consistently outperforming full fine-tuning across GLUE benchmarks with BERT and RoBERTa. https://hackernoon.com/smarter-ai-training-with-few-shot-natural-language-tasks #fewshotlearning

AdaMix proves its edge in few-shot NLU, consistently outperforming full fine-tuning across GLUE benchmarks with BERT and RoBERTa. https://hackernoon.com/smarter-ai-training-with-few-shot-natural-language-tasks #fewshotlearning

AdaMix improves fine-tuning of large language models by mixing adaptation modules—outperforming full tuning with just 0.2% parameters. https://hackernoon.com/beating-full-fine-tuning-with-just-02percent-of-parameters #fewshotlearning

AdaMix improves fine-tuning of large language models by mixing adaptation modules—outperforming full tuning with just 0.2% parameters. https://hackernoon.com/beating-full-fine-tuning-with-just-02percent-of-parameters #fewshotlearning

Ablation studies on AdaMix reveal why adaptation merging, consistency regularization, and module sharing drive superior fine-tuning performance. https://hackernoon.com/the-role-of-consistency-and-sharing-in-efficient-fine-tuning #fewshotlearning

Ablation studies on AdaMix reveal why adaptation merging, consistency regularization, and module sharing drive superior fine-tuning performance. https://hackernoon.com/the-role-of-consistency-and-sharing-in-efficient-fine-tuning #fewshotlearning

AdaMix outperforms fine-tuning and top PEFT methods across NLU, NLG, and few-shot NLP tasks, proving both efficient and powerful. https://hackernoon.com/smarter-fine-tuning-for-nlu-and-nlg-tasks #fewshotlearning

AdaMix outperforms fine-tuning and top PEFT methods across NLU, NLG, and few-shot NLP tasks, proving both efficient and powerful. https://hackernoon.com/smarter-fine-tuning-for-nlu-and-nlg-tasks #fewshotlearning

Discover how Mixture-of-Adaptations uses random routing and weight merging to fine-tune language models with less cost and better performance. https://hackernoon.com/how-mixture-of-adaptations-makes-language-model-fine-tuning-cheaper-and-smarter #fewshotlearning

Discover how Mixture-of-Adaptations uses random routing and weight merging to fine-tune language models with less cost and better performance. https://hackernoon.com/how-mixture-of-adaptations-makes-language-model-fine-tuning-cheaper-and-smarter #fewshotlearning

AdaMix fine-tunes large language models with just 0.1% of parameters, beating full fine-tuning in performance and efficiency. https://hackernoon.com/how-to-improve-ai-models-while-training-only-01percent-of-parameters #fewshotlearning

AdaMix fine-tunes large language models with just 0.1% of parameters, beating full fine-tuning in performance and efficiency. https://hackernoon.com/how-to-improve-ai-models-while-training-only-01percent-of-parameters #fewshotlearning

Собственный контент-фильтр на базе LLM: от эксперимента до стабильной системы

Привет! Меня зовут Миша Мартьянов, я инженер по исследованиям и разработке в red_mad_robot. Моя работа — искать новые идеи, проверять гипотезы и улучшать продукты. На этом пути иногда приходится изобретать уникальные решения. Например, мы создали собственный фильтр, чтобы отсеивать нежелательный контент с помощью LLM. Рассказываю, как мы к этому пришли и с какими сложностями столкнулись.

https://habr.com/ru/companies/redmadrobot/articles/922680/

#ai #llm #фильтр_контента #fewshotlearning #fewshot #false_positive #filter

Не любой In-context learning одинаково полезен

Промпт-инжиниринг (Prompt engineering) - широко используемая техника для улучшения качества генерации LLM. Few-shot learning и Chain-of-thought - основные техники современного промпт-инжиниринга. Оказывается, не любые Few-shot prompting и Chain-of-thought одинаково полезны и могут принести свои биасы в генерацию модели и испортить всю магию от их применения.

https://habr.com/ru/articles/832310/

#llm #ai_alignment #ai #искусственный_интеллект #chain_of_thoughts #fewshotlearning