У нас вы можете посмотреть бесплатно RLHF: Training Language Models to Follow Instructions with Human Feedback - Paper Explained или скачать в максимальном доступном качестве, видео которое было загружено на ютуб. Для загрузки выберите вариант из формы ниже:
Если кнопки скачивания не
загрузились
НАЖМИТЕ ЗДЕСЬ или обновите страницу
Если возникают проблемы со скачиванием видео, пожалуйста напишите в поддержку по адресу внизу
страницы.
Спасибо за использование сервиса ClipSaver.ru
In this video we talk about how we can train large language models (LLMs) to follow instructions with human feedback. The paper proposes a solution called InstructGPT, which involves fine-tuning GPT-3 using human feedback to align the model with user intent across various tasks. By collecting datasets of labeler demonstrations and rankings of model outputs, the InstructGPT model, despite having fewer parameters than GPT-3, shows preference in human evaluations and improvements in truthfulness and reduction in toxic output generation. References ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ “Training language models to follow instructions with human feedback” paper: https://arxiv.org/pdf/2203.02155.pdf Related Videos ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Chain-of-Verification (COVE) Reduces Hallucination in Large Language Models: • Chain-of-Verification (COVE) Reduces ... Why Language Models Hallucinate: • Why LLMs Hallucinate Transformer Self-Attention Mechanism Explained: • Transformer Self-Attention Mechanism ... Jailbroken: How Does LLM Safety Training Fail? - Paper Explained: • Jailbroken: How Does LLM Safety Train... How to Fine-tune Large Language Models Like ChatGPT with Low-Rank Adaptation (LoRA): • Low-Rank Adaptation (LoRA) Explained Multi-Head Attention (MHA), Multi-Query Attention (MQA), Grouped Query Attention (GQA) Explained: • Multi-Head Attention (MHA), Multi-Que... LLM Prompt Engineering with Random Sampling: Temperature, Top-k, Top-p: • LLM Prompt Engineering with Random Sa... Contents ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 00:00 - Abstract & Intro 03:01 - Main Results - Human Preferences 04:45 - RLHF Overview 07:13 - Methods and Experiments 14:32- Results 18:45 - Discussion & Conclusions Follow Me ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 Twitter: @datamlistic / datamlistic 📸 Instagram: @datamlistic / datamlistic 📱 TikTok: @datamlistic / datamlistic Channel Support ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon: / datamlistic ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a #llm #rlhf