Train Large Language Models using Reinforcement Learning from Human Feedback

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In this blogpost we will dive into the world of Natural Language Processing (NLP) and other task-oriented Machine Learning (ML) solutions.

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‍It's been a few months since the launch of ChatGPT, and most recently GPT-4, AI Experts have seen firsthand the power of LLMs (Large Language Models) in the AI for Language industry. GPT and other proprietary models are great starting points for using LLMs for business needs.

What is a Large Language Model (LLM)?

Before we dive deeper into how to build your LLM, let's first take a look at the basics. LLMs are computer programs that can process natural language text and generate outputs to inputs (generate answers to questions in case of chatGPT-style LLMs). They're used in a variety of applications, such as chatbots, question-answering systems, and language translation services.

How to develop a LLM

But what if you want to build your own LLM? You are going to need a framework to generate training data and fine-tune your models.

‍‍To build an LLM, you need two things: data and a model. The data is used to train the model, which will be able to generate responses to queries. The model can be a neural network, a machine learning algorithm, or any other type of computational system that can learn from data.

Where to Find Large Language Models

‍Now, let's get to M47AI Platform. Our platform provides you with a comprehensive set of tools to build and train your LLM. With our platform you can:

• Create custom datasets
• Train models
• Fine-tune them for your specific use case‍

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One of the key features of our platform is reinforcement learning with human feedback (RLHF). This is a method that significantly addresses the practical limitations inherent in earlier instruction-tuned models. Models trained with RLHF can produce responses that correspond to human values, offer more detailed answers, and decline queries that are either unsuitable or beyond the model's knowledge scope.

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To help you get started with RLHF, we've created a 3-step-guide that will walk you through the process:

• First, you'll need to pretrain your language model.
• Then, you'll need to create a dataset of inputs and outputs that are relevant to your use case and train a reward model.
• Finally, you'll fine-tune your model to improve its accuracy and performance.

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Using this method significantly addressed the practical limitations inherent in earlier instruction-tuned models. Models trained with RLHF can produce responses that correspond to human values, offer more detailed answers, and decline queries that are either unsuitable or beyond the model's knowledge scope. For further reading, check out Hugging Face blogpost about illustrating RLHF.

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The world of LLMs is an exciting and rapidly growing field. With M47AI, you can be at the forefront of this technology and use it to your business's advantage. Whether you're a large corporation or a small business owner, our NLP platform is the perfect tool to help you build a powerful LLM that can take your business to the next level.

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