Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟱𝟯 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Meta AI Introduces A New AI Technology Called ‘Few-Shot Learner (FSL)’ To Tackle Harmful Content Follow me for a similar post: 🇮🇳 Ashish Patel 🇮🇳 ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗘𝗻𝘁𝗮𝗶𝗹𝗺𝗲𝗻𝘁 𝗮𝘀 𝗙𝗲𝘄-𝗦𝗵𝗼𝘁 𝗟𝗲𝗮𝗿𝗻𝗲𝗿 🔸 This paper is published arxiv2021. 🔸 For the training of AI models, a massive number of labeled data points or examples are required. Typically, the number of samples needed is tens of thousands to millions. Collection and labeling of these data can take several months. This manual collection and labeling delay the deployment of AI systems that can detect new types of harmful content over different social media platforms. To handle this issue, Meta has deployed a relatively new AI model called “Few-Shot Learner” (FSL) such that harmful contents can be detected even if enough labeled data is not available. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Large pre-trained language models (LMs) have demonstrated remarkable ability as few-shot learners. 🔹However, their success hinges largely on scaling model parameters to a degree that makes it challenging to train and serve. 🔸In this paper, we propose a new approach, named as EFL, that can turn small LMs into better few-shot learners. The key idea of this approach is to reformulate potential NLP task into an entailment one, and then fine-tune the model with as little as 8 examples. 🔹We further demonstrate our proposed method can be: (i) naturally combined with an unsupervised contrastive learning-based data augmentation method; (ii) easily extended to multilingual few-shot learning. 🔸A systematic evaluation on 18 standard NLP tasks demonstrates that this approach improves the various existing SOTA few-shot learning methods by 12\%, and yields competitive few-shot performance with 500 times larger models, such as GPT-3. ------------------------------------------------------------------- #computervision #artificialintelligence #innovation -------------------------------------------------------------------