#NewsBytesExplainer: Internet is loving OpenAI's ChatGPT chatbot. What's so special?
As the world awaits OpenAI's GPT-4, the company has quietly rolled out GPT-3.5, an improved version of its GPT-3 engine. A part of GPT-3.5 is ChatGPT, an interactive, general-purpose, AI-based chatbot that can write code, solve problems, and provide customer support. The chatbot is in a public demo and can be used freely now. Let's take a look at why it is special.
ChatGPT can engage in human-like conversations
In its original form, GPT-3 is capable of predicting what text follows a string of words. On the other hand, although trained on GPT-3.5, ChatGPT is trained to provide more conversational answers. This means that the AI is capable of answering follow-up questions. The bot tries to engage with users in a more human-like fashion. This results in fluid conversations.
The chatbot can remember conversations and recount them later
ChatGPT's conversational model means that it not only is capable of answering follow-up questions, but also can "admit its mistakes, challenge incorrect premises, and reject inappropriate requests." The last one is an important aspect that makes ChatGPT stand out from its predecessors and contemporaries. We will get into that later. The chatbot can also remember what was said earlier and recount it later.
The chatbot can improve codes and even write new ones
People have been putting ChatGPT through the ropes as it is now available for free testing. Users have found out that it can write poetry, correct coding mistakes, write new code, explain scientific concepts, write essays, and more. It also has a solution for one of the pertinent problems of large language models - reigning in the offensive proclivities.
The chatbot can also write scripts for TV shows
And, it can code with ease
ChatGPT won't answer potentially harmful questions
ChatGPT won't answer your potentially harmful questions. It is trained to avoid giving answers on controversial topics. For instance, it won't answer you if you ask about how to make a bomb. If you ask questions about race or religion, it will give you boilerplate answers. The question is, how did OpenAI achieve this?
OpenAI used reinforced learning from human feedback on ChatGPT
ChatGPT's ability to avoid potentially harmful questions is a result of reinforcement learning from human feedback (RLHF) and through a special prompt, it prepends to every input. RLHF is the same method that OpenAI used for InstructGPT but with a slightly different data collection setup. Let's take a look at how OpenAI controls ChatGPT's responses.
How was ChatGPT trained?
OpenAI used supervised fine-tuning on an initial model, where human AI trainers provided conversations in which they played both user and AI assistant to improve the bot's understanding of human conversations and responses. The company created a reward model for reinforcement learning by collecting comparison data. The trainers then ranked the best to worst outputs.
OpenAI uses Proximal Policy Optimization for reinforcement learning
OpenAI has been using Proximal Policy Optimization (PPO) for reinforcement learning. The company initialized the PPO model from the supervised policy. The policy then generated an output. This was again ranked by AI trainers. A reward is calculated for every output. With the help of these reward models, the model is fine-tuned. The company did several iterations of this.
ChatGPT's restrictions can still be circumvented
Sure, OpenAI used reinforcement learning to control ChatGPT's responses but some users have already found a loophole in this. You can make the AI ignore its restrictions through some trickery. For instance, you can ask the AI to pretend like it's a character in a film or how an AI model "shouldn't" respond to a certain question. This will help circumvent ChatGPT's safety.
The AI is smart but it can be tricked
ChatGPT suffers from same limitations as other chatbots
ChatGPT is better than other chatbots trained on large language models. However, it suffers from the same issues as others. For instance, it sometimes presents false or invented information very confidently. The model is also sensitive to phrasing. Depending on that, it may change its answers. In case of ambiguity, it tries to gather the user's intention instead of asking follow-up questions.
