Perspectives from the frontier
Confidence Needs Calibration
Frontier labs spend billions on reasoning and accuracy. Almost nobody trains models to know when to say, "I'm not sure."
You Can't Prompt Your Way to Safety
AI models are giving medical and mental health advice to millions of people. Can you prevent harmful advice by adding safety instructions to the prompt? The UK's AI Safety Institute (AISI) recently tested this.
90% of Human Expertise Is Not Verifiable
RLVR's verification crisis exposes a fundamental gap in how AI measures expert judgment across professional domains
Advancing AI Alignment through Human-Judged LLM Debates
Learn how Pareto helped MATS obtain high quality data for their research.
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
Designing Robust Human Studies for AI Safety Evaluations
A comprehensive guide to identifying vulnerabilities in AI models through systematic jailbreaking research, exploring methodologies, challenges, and potential defenses.
The Ultimate Guide to Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) merges retrieval-based models, which fetch relevant information from a database, with generation-based models like GPT, which generate text. It begins by retrieving pertinent documents based on a query. Then, it uses this retrieved information alongside the query to produce a response. This fusion allows RAG to provide accurate, diverse, and contextually appropriate responses, making it effective for tasks like question answering and content generation.
Should You Pay Per Task or By Hour? Optimizing Worker Productivity for High-Quality Data
Expert labelers favor payment per task over hourly wages for high-quality data annotation despite published research. Gain insights into the contrasting influence of pay-per-task and hourly wage compensation structures for data labeler productivity.
Reinforcement Learning from Human Feedback: Everything You Need to Know
In this blog, we explain what RLHF is, its applications in the world of machine learning, and how it enables agents to optimize their decisions through human-guided interactions, enhancing their performance and real-world relevance.