Pareto Blog | Large Language Models

Annotation fatigue: Why human data quality declines over time

Learn how prolonged annotation tasks lead to fatigue, reduced data quality, and slower output, and discover research-backed strategies Pareto AI uses to keep annotators engaged.

The micro-decisions made by AI trainers that define data quality

Discover how micro-decisions by AI trainers shape data quality, safety, and alignment in LLMs.

The false dichotomy of "synthetic data vs. human data"

We provide actionable strategies on how AI companies can effectively combine synthetic and human data to enhance model performance

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