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Discover the basics of a vital technique behind the success of next-generation AI tools like ChatGPT
The massive adoption of tools like ChatGPT and other generative AI tools has resulted in a huge debate on the benefits and challenges of AI and how it will reshape our society.
To better assess these questions, it’s important to know how the so-called Large Language Models (LLMs) behind next-generation AI tools work.
This article provides an introduction to Reinforcement Learning from Human Feedback (RLHF), an innovative technique that combines reinforcement learning techniques and human guidance to help LLMS like ChatGPT deliver impressive results.
We will cover what RLHF is, its benefits, limitations, and its relevance in the future development of the fast-paced field of generative AI. Keep reading!
To understand the role of RLHF, we first need to speak about the training process of LLMs.
The underlying technology of the most popular LLMs is a transformer.
Since its development by Google researchers, transformers have become the state-of-the-art model in the field of AI and deep learning, as they provide a more effective method to handle sequential data, like the words in a phrase.