Introduction

By means of this thorough guidebook, we shall interpret ⁠ the methods undertaken by OpenAI for crafting ChatGPT. For creating specialized intelligent bots, we will follow these steps ⁠ while utilizing GPT-2 as our preferred language model. To train and optimize a closed-domain single turn question answering bot, ⁠ we will make use of reinforcement learning in AWS SageMaker. Throughout this article, you will gain knowledge about important ideas including RLHF, PPO, ⁠ prompts for learning and prompt engineering, Kullback-Leibler (KL) divergence, and more. Examples and Jupyter notebooks that are practical will be provided by us For ⁠ supporting your venture in creating an intelligent bot application on AWS.

The Rise of ChatGPT ​

n November 2022, OpenAI introduced ChatGPT as a highly advanced Large Language Model (LLM) developed ⁠ by OpenAI and It was released during the month of November in 2022. The model gained massive popularity, attaining one ⁠ million users in just five days. By January 2023, it also achieved the milestone of having 100 million ⁠ monthly users., as per the evaluations made by UBS analysts. ChatGPT is Created with a basis ⁠ in the GPT-3 series model. Involving explicit instructions during the training process, it ⁠ adopts a similar training approach as InstructGPT.

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Understanding Reinforcement Learning ‌

Before diving into building our bot, let’s obtain ⁠ a rudimentary comprehension of reinforcement learning (RL). The agent is responsible for making decisions, The ⁠ environment gives feedback through rewards or penalties. Within our specific scenario, we aim to investigate the potential applications of ⁠ RL in language models and assignments centered around interpreting natural language. ​

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Building a FAQ Bot with GPT-2

Creating an AI Chatbot for Answering Common ⁠ Questions taking advantage of GPT-2 ‌
Our attention in this part will be directed towards constructing a chatbot that specializes in answering ⁠ queries within a specific field, utilizing GPT-2 as the foundation for our language model. Differing from open-domain chatbots, Our FAQ bot’s main objective is to provide precise ⁠ responses within a closed domain, in our case, related to COVID-19. ​

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Step 1: Self-Supervised Pre-Training

For the commencement of our bot-building exercise, we will commence by ⁠ pre-training the GPT-2 model on news articles related to COVID-19. We will utilize SageMaker Processing and SageMaker ⁠ Distributed Training for this pre-training pipeline. Our custom GPT-2 model’s foundation will be built upon by undertaking this crucial ⁠ step, Designed specifically to cater to the demands of our particular domain. ‌

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Step 2: Supervised Fine-Tuning (SFT)

Now that we have our tailor-made GPT-2 model, the following action involves optimizing its performance in providing accurate responses to commonly asked questions about COVID-19 through training with a dataset focused on ⁠ this subject We will Conduct prompt-based learning with the aim of templating and tokenizing the QA pairs, Ensuring they are properly prepared before entering into a program of supervised fine-tuning. This step ensures that our bot becomes aligned for quality ⁠ and generates responses that adhere to human-preferred closeness. ⁠

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Step 3: Human Feedback ‌

The quality and coherence of our bot’s ⁠ responses depend on human feedback. Our approach involves simulating human labeling and use a variant ⁠ model along with our SFT model to generate responses. Human labelers will provide their preference ratings for these responses, ⁠ helping us identify the best answers for each prompt.

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Step 4: Building the Reward ⁠ Preference Model (RPM) ‍

To score the quality of our bot’s responses, we ⁠ plan on developing a reward preference model (RPM). The RPM will output scalar reward ⁠ scores based on prompt-response pairs. The RPM will be trained using a BERT-base model with a classification head, ⁠ leveraging the power of pre-trained language models to evaluate response quality. ​

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Step 5: Reinforcement Learning from ⁠ Human Feedback (RLHF) ‍

The final step involves utilizing the SFT and RPM models to train a ⁠ reinforcement learning (RL) policy that optimizes based on the reward model. RLHF greatly depends on Proximal Policy ⁠ Optimization (PPO) and KL divergence. Giving the model the capability to optimize for human ⁠ preferences results in more precise and beneficial responses. ‌

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Conclusion ‍

By implementing the instructions in this specialized manual, you will develop a strong grasp ⁠ of constructing industry-specific smart chatbots employing GPT and reinforcement learning on AWS SageMaker. With the inclusive steps provided and real-life illustrations, you can develop your ⁠ very own intelligent bot application that suits your unique domain requirements. Embrace the power of language models and advance ⁠ your conversational AI applications to new heights. ‌