Pareto Blog | Large Language Models

26 Prompting Principles for Optimal LLM Output

Discover 26 essential prompting principles to enhance your interactions with large language models (LLMs). Learn how to craft precise prompts for clearer, more accurate AI-generated responses.

Is Data Scarcity the Biggest Obstacle to AI’s Future?

We delve into the implications of data scarcity on model training, emphasizing the need for high-quality, expert-sourced human data as a cornerstone of AI development. We also explore how supplementing expert-led data collection with synthetic data can be a viable strategy for addressing these challenges.

Apple's AI Ambitions: DCLM-7B, Data Curation, and Consumer Tech

Apple's DCLM-7B sets a new AI standard with thoughtful data curation. Explore its impact, transparency, and the role of expert data in our latest blog.

Leveraging OpenAI o1's "Deep Thinking" Capabilities Effectively

With the introduction of OpenAI o1's reasoning capability, prompting methods need to be adjusted. OpenAI o1 handles complex reasoning internally, which means old prompting strategies may no longer be effective. Understanding these shifts is key to leveraging the model’s strengths optimally.

Federated Learning in Computer Vision Explained

This article discusses how federated learning changes computer vision by training AI models without sharing raw data. It solves privacy issues and improves model accuracy, using examples like smartphones that are getting better at predicting text. We cover how federated learning works, its challenges, and how to solve them. Finally, we look at real-world uses in medical imaging, smart surveillance, self-driving cars, retail, farming, and smart home device

Beginners Guide to One-shot Learning

In the article, we discuss one-short learning, a computer vision model that uses only one example per data category instead of many to teach machine models. We go deeper to compare its counterparts and also check out its use cases.

The Complete Guide to Few-Shot Learning

Few-shot learning is a machine learning model that works with few labeled examples. The article describes how few-shot learning is used in various fields, such as natural language processing, computer vision, healthcare, and speech recognition. We outline different approaches, including meta-learning, data-level methods, parameter-level methods, generative techniques, and more that you need to check.

Understanding Model Drift In Machine Learning

In this guide, we'll explore different types of model drift, including concept and data drift, and discuss how to detect and tackle these issues. We'll also share some practical strategies for continuous retraining, model versioning, and monitoring performance metrics to keep your machine-learning models effective over time.