There have been huge developments in the field of AI in the past ten years. Models taught with modern deep learning techniques can produce creative and visually stunning works of art, such as new poems, songs, and visuals. Among such deep learning models are large language models that are being used as chatbots. Inadvertently, these models frequently exhibit characteristics like fabricating facts, producing biassed or toxic content, or simply not following user instructions. There has been tremendous effort put towards training language models to coincide with the user’s purpose, which has led to some promising results. This includes both overt actions like complying with orders and covert ones like telling the truth and avoiding bias, toxicity, and harm. One such AI tool, called ChatGPT, developed and launched by OpenAI in November 2022, has been garnering a huge amount of attention from the public. ChatGPT is a chatbot-like service that uses a custom-built GPT model to have natural conversations with users and respond to their questions or requests for help over a wide range of topics. In order to make the bot trustworthy, it has been programmed to ignore harmful queries and give only accurate, unbiased responses.
ChatGPT And Its Features
Incorporating both supervised and reinforcement learning methods, ChatGPT is a cutting-edge conversational AI tool trained on GPT-3.5, an enhanced version of GPT-3. ChatGPT is a better version of its predecessor, InstructGPT. Like InstructGPT, ChatGPT is taught to answer a question by following the steps in the instruction.ChatGPT, in contrast to InstructGPT, offers more precise responses and refuses to fulfill destructive, toxic, or unsuitable requests for help. It covers comprehensive information on a wide variety of subjects, from computer programming to films. Lacking access to the internet, it is unable to provide answers to questions about events that occurred after it was optimized in 2021. However, the chatbot has been doing a great job at answering queries from a wide variety of categories, including programming, health, finance, life, general knowledge, and much more.
Try the ChatGPT tool here!
Generative Pre-trained Transformer is an autoregressive language model employing an unsupervised pre-training and supervised fine-tuning strategy. In the first stage of training, the model is taught to generalize from a vast body of text, and then it is fine-tuned using labeled data in order to perform discriminative tasks. A primary focus during the GPT model’s development and training was the acquisition of a generalizable representation that requires minimum tuning for use across a wide variety of applications. At its most fundamental level, the model architecture employs a multi-layer (12-layer) transformer decoder, a variant of the transformer model that performs a multi-headed self-attention operation on the input tokens. Multiple iterations of this original language model yielded GPT-2, GPT-3, and GPT-3.5, each of which had more parameters and better performance than the previous iteration. If you want to learn more about the GPT-3 model, I highly recommend reading this fantastic article!
Supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) fine-tuning were used to train the pre-trained generalized GPT-3.5 model to make it behave like a chatbot. To begin, the initial model is trained using human trainer-provided conversations in which humans play both the user and the chatbot in order to first teach the model of the desired behavior through supervised learning. Additionally, model-written suggestions were offered to the trainers to help them craft their responses in a manner similar to the AI assistant. Following that, a prompt from the dataset is used to generate multiple model responses, which are then ranked by human labelers from most to least preferred output. With this information, a reward model is trained to predict the most preferred output based on the scalar reward value given by the RM. Finally, proximal policy optimization (PPO), a reinforcement learning technique, is optimized using these reward models to produce the best possible response from the model. PPO is a policy gradient method to further fine-tune the SFT model developed in the first stage. The PPO model is initialized from the SFT model, which is then instructed to generate an answer to the input prompt; this answer is then fed into the reward model, which determines an appropriate reward value. Several cycles of PPO are then used to refine the policy based on the new information, and only then is the model deployed online.
Though OpenAI’s researchers have trained ChatGPT to produce appropriate and trustworthy responses free of bias and toxicity, the system still has significant shortcomings that they are working to address. Due to the present fine-tuning procedures, the model may occasionally provide incorrect or non-factual responses and overuse specific phrases while responding to different prompts. Fixing this will necessitate revising the fine-tuning functions and the training data currently being used for fine-tuning. There is also some sensitivity on the model’s part to how the question is phrased. Responses to two similar requests can vary greatly depending on the phrasing used. The OpenAI group additionally employs the Moderation API, a GPT-based classifier that determines whether the material is sexual, hateful, aggressive, or supports self-harm, to prevent any unsuitable prompt or response from being displayed in ChatGPT. However, it does occasionally react to such hints or provides biased answers. The team is currently refining the model based on the input and feedback collected from the current users of the tool.
In just a week, ChatGPT has already amassed over a million online users. It has also been considered a possible rival to Google. The chatbot can compose highly sophisticated essays and detailed descriptions on any subject. It provides insightful guidance on matters of finance, education, life, and self-improvement; critiques books and films; teach you about any topic linked to any field; translates languages, and can produce beautiful poems and stories based on any context offered by the user. The marketing, customer service, call center, content development, and technology industries can all greatly benefit from this. It can function as an interpreter for code, help developers troubleshoot and solve bugs in their code, and even translate code between various programming languages. For developers everywhere, this might be a game-changer. A variety of chatbots that function like AI assistants have been built using the ChatGPT API, and they’re already available for use on messaging apps like WhatsApp and Telegram.
ChatGPT has enormous promise, but it also has its own drawbacks and problems if it is misused. Academic institutions are already skeptical and condemning the possible use of ChatGPT by students to write their assignments and projects. In order to move forward with this, educational institutions will need to adjust their grading policies to take ChatGPT into consideration. This also has a negative impact on content creators because it means that anyone can publish thoughtful, in-depth articles on any subject. Models to determine if a piece of literature is the product of artificial intelligence or human authors have been the subject of ongoing study. To further distinguish the work of ChatGPT and other OpenAI chatbots, cryptographic watermarks have been developed. The possibility of providing a wrong answer is another restriction that has been a barrier for ChatGPT. There have been cases of the tool producing wrong answers to data that it has not been trained on, despite the OpenAI team’s notable efforts to fine-tune the model to prevent inaccurate answers. Worse yet, it responds in such an authoritative and confident way that it makes the counterclaim sound like it must be true. For the same reason, the AI tool ChatGPT was recently barred from providing responses on the question-and-answer website Stackoverflow due to excessive submissions of incorrect answers.
There is no avoiding the inevitable march of AI forward, but it is nonetheless vital that we comprehend the nature of AI and learn to tell it apart from human talents. The primary goal of developing more advanced AI models is not to diminish, hurt, or restrict human capabilities, but rather to improve and augment them. Did you try out ChatGPT yet? Let us know your views in the comments section.
- Transformer Model – Attention Is All You Need
- GPT Model – Improving Language Understanding by Generative Pre-Training
- GPT-2 Model – Language Models are Unsupervised Multitask Learners
- GPT-3 Model – Language Models are Few-Shot Learners
- Fine-Tuning Strategies – Learning to summarize from human feedback
- PPO – Proximal Policy Optimization Algorithms
- InstructGPT – Training language models to follow instructions with human feedback
- Moderation API – A Holistic Approach to Undesired Content Detection in the Real World