Mastodawn

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

metaend Feb 6

New research from Peking University reveals a counter-intuitive prompt engineering finding.

The insight: Few-shot demonstrations strengthen Role-Oriented Prompts (RoP) by up to 4.5% for jailbreak defense. Same technique degrades Task-Oriented Prompts (ToP) by 21.2%.

The mechanism: Role prompts establish identity. Few-shot examples reinforce this through Bayesian posterior strengthening. Task prompts rely on instruction parsing. Few-shot examples dilute attention, creating vulnerability.

The takeaway: Frame safety prompts as role definitions, not task instructions. Add 2-3 few-shot safety demonstrations. Avoid few-shots with task-oriented safety prompts.

Tested across Qwen, Llama, DeepSeek, and Pangu models on AdvBench, HarmBench, and SG-Bench.

Paper: arXiv:2602.04294v1

#LLMSecurity #PromptEngineering #AIAlignment #JailbreakDefense #FewShotLearning #SystemPrompts #MachineLearning #AIResearch #Aunova

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HackerNoon Oct 2

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

Smarter AI Training with Few-Shot Natural Language Tasks | HackerNoon

AdaMix proves its edge in few-shot NLU, consistently outperforming full fine-tuning across GLUE benchmarks with BERT and RoBERTa.

HackerNoon Oct 2

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

Beating Full Fine-Tuning with Just 0.2% of Parameters | HackerNoon

AdaMix improves fine-tuning of large language models by mixing adaptation modules—outperforming full tuning with just 0.2% parameters.

HackerNoon Oct 1

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

The Role of Consistency and Sharing in Efficient Fine-Tuning | HackerNoon

Ablation studies on AdaMix reveal why adaptation merging, consistency regularization, and module sharing drive superior fine-tuning performance.

HackerNoon Oct 1

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

Smarter Fine-Tuning for NLU and NLG Tasks | HackerNoon

AdaMix outperforms fine-tuning and top PEFT methods across NLU, NLG, and few-shot NLP tasks, proving both efficient and powerful.

HackerNoon Oct 1

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

How Mixture-of-Adaptations Makes Language Model Fine-Tuning Cheaper and Smarter | HackerNoon

Discover how Mixture-of-Adaptations uses random routing and weight merging to fine-tune language models with less cost and better performance.

HackerNoon Oct 1

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

How to Improve AI Models While Training Only 0.1% of Parameters | HackerNoon

AdaMix fine-tunes large language models with just 0.1% of parameters, beating full fine-tuning in performance and efficiency.

Habr Jun 27, 2025

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

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

Хабр

Doug Ortiz Feb 14, 2025

🤖💡 Few-Shot Learning: Learn More with Less Data! 💡🤖

After a year exploring few-shot learning, I've created a guide to help you:
📌 Understand the basics & key algorithms (Prototypical/Matching Networks)
📌 Avoid pitfalls like overfitting & poor preprocessing
📌 Boost results w/ transfer & semi-supervised learning 🔄

👀 Watch the 🔗 full video 👉: https://link.illustris.org/aiminimaldata
#AI #FewShotLearning #MachineLearning #TechTips 🤖✨

Discover How AI Learns with Minimal Data

🔍 Few-shot & Zero-shot Learning: How AI Thinks Like Us!Large Language Models (LLMs) like ChatGPT can understand and generate text, but how do they handle br...

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