LLMs Don’t Just Need to Be Smart — They Need to Be Specific. Here’s How.

Author(s): Kaushik Rajan

Originally published on Towards AI.

How a new technique called “Test-Time Deliberation” teaches AI to think before it speaks

I spend a lot of my time wrestling with Large Language Models (LLMs). The goal is always the same: how do we get these incredibly powerful, general-purpose engines to do exactly what we need in a specific situation, safely and reliably? It’s a surprisingly tricky problem.

The article explores the challenges of utilizing Large Language Models (LLMs) effectively, especially in varying contexts that require different operational rules. It introduces “Test-Time Deliberation” (TTD), a method that enhances LLMs’ ability to generate responses by enabling them to deliberate on instructions in real-time, thereby improving specificity and safety. This technique aims to provide a more dynamic and adaptable AI, ultimately leading to better alignment with intricate safety guidelines and user instructions while ensuring effective performance across diverse applications.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI