The evolution of curated interactions, particularly in the context of ChatGPT and similar language models, has been an ongoing process aimed at improving the quality and safety of user interactions.
ChatGPT was designed to generate human-like responses to user inputs by utilizing a massive amount of training data. However, it became evident that without proper oversight, the model could sometimes produce inaccurate, biased, or inappropriate responses. OpenAI, the organization behind ChatGPT, recognized these concerns and took steps to address them.
To ensure a safer and more reliable user experience, OpenAI implemented a two-step approach: pre-training and fine-tuning. During pre-training, ChatGPT was exposed to a large corpus of publicly available text from the internet, which helped it learn grammar, facts, and some reasoning abilities. However, this unsupervised training process also meant that the model might absorb biased or harmful information.
To mitigate these issues, OpenAI employed fine-tuning, which involved curating a dataset with human reviewers. These reviewers followed guidelines provided by OpenAI to review and rate possible model outputs for a range of example inputs. The iterative feedback process between OpenAI and the reviewers helped improve the model’s performance over time.
OpenAI recognized that transparency and user control were vital aspects of the system. As a result, they launched the ChatGPT API in a research preview phase, providing access to developers and users. This allowed OpenAI to gather valuable feedback and iterate on the system further.
Beyond the initial release of ChatGPT, OpenAI has been actively exploring methods to reduce biases and improve default behavior. They have been investing in research and engineering to make the fine-tuning process more understandable and controllable. They have also solicited public input on various aspects, such as system behavior and deployment policies, to ensure a broader perspective
