We've trained a model called ChatGPT which interacts in a conversational way, which makses it possible to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
In the following sample, ChatGPT asks the clarifying questions to debug code.
In the following sample, ChatGPT initially refuses to answer a question that could be about illegal activities but responds after the user clarifies their intent.
In the following sample, ChatGPT is able to understand the reference (“it”) to the subject of the previous question.
TRUSTED BY 100,000+ TEAMS GLOBALLY AT INNOVATIVE COMPANIES INCLUDING...
ChatGPT's release is the latest step in OpenAI's deployment of safer and more useful AI systems. Lessons from earlier models like GPT-3 and Codex have informed safety measures, including reducing harmful outputs through reinforcement learning from human feedback (RLHF).
Jack Daniels
Manager, Marketing Operations
Are there any limitations of ChatGPT?
There are some limitations of ChatGPT which include generation of content that is xincorrect or biased. Here are a few examples of such situations:
ChatGPT is sensitive to tweaks to the input phrasing or attempting the same prompt multiple times.
The model is often excessively verbose and overuses certain phrases, such as restating that it’s a language model trained by OpenAI. These issues arise from biases in the training data and well-known over-optimization issues.
Ideally, the model would ask clarifying questions when the user provided an ambiguous query. Instead, our current models usually guess what the user intended.
ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers.
How was the model trained to perform all its tasks?
We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them compose their responses. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format. To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. To collect this data, we took conversations that AI trainers had with the chatbot. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. Using these reward models, we can fine-tune the model using Proximal Policy Optimization. We performed several iterations of this process.
How is ChatGPT different than InstructGPT?
We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them compose their responses. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format. To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. To collect this data, we took conversations that AI trainers had with the chatbot. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. Using these reward models, we can fine-tune the model using Proximal Policy Optimization. We performed several iterations of this process.
Is ChatGPT a paid service?
We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them compose their responses. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format. To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. To collect this data, we took conversations that AI trainers had with the chatbot. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. Using these reward models, we can fine-tune the model using Proximal Policy Optimization. We performed several iterations of this process.