AI Modeling Data Scarcity NLP 
Towards AI
OCTOBER 31, 2024
GenAI can help by automatically clustering similar data points and inferring labels from unlabeled data, obtaining valuable insights from previously unusable sources. Natural Language Processing (NLP) is an example of where traditional methods can struggle with complex text data. GPT-4o mini response use case #2.
Marktechpost
JULY 8, 2024
Multilingual natural language processing (NLP) is a rapidly advancing field that aims to develop language models capable of understanding & generating text in multiple languages. These models facilitate effective communication and information access across diverse linguistic backgrounds.
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Topbots
DECEMBER 19, 2023
The findings indicate that alleged emergent abilities might evaporate under different metrics or more robust statistical methods, suggesting that such abilities may not be fundamental properties of scaling AI models. The paper also explores alternative strategies to mitigate data scarcity.
Pickl AI
MAY 29, 2024
Also read: What is Information Retrieval in NLP? What is Tokenization in NLP? Instead of training separate models for each task, we can train a single model for multiple tasks, leading to significant time, memory, and energy savings. By simultaneously tackling multiple related tasks, MTL offers a myriad of benefits.
Pickl AI
FEBRUARY 1, 2023
Transfer Learning is a technique in Machine Learning where a model is pre-trained on a large and general task. Since this technology operates in transferring weights from AI models, it eventually makes the training process for newer models faster and easier. Thus it reduces the amount of data and computational need.
Pickl AI
OCTOBER 9, 2024
This blog explores the innovations in AI driven by SLMs, their applications, advantages, challenges, and future potential. What Are Small Language Models (SLMs)? Small Language Models (SLMs) are a subset of AI models specifically tailored for Natural Language Processing (NLP) tasks.
Viso.ai
DECEMBER 18, 2023
Instead of relying on organic events, we generate this data through computer simulations or generative models. Synthetic data can augment existing datasets, create new datasets, or simulate unique scenarios. Specifically, it solves two key problems: data scarcity and privacy concerns.
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