I asked ChatGPT if it will tell me about daily news in the future and more.

CJ: “How is ChatGPT trained?”

ChatGPT: “ChatGPT is a pre-trained language model developed by OpenAI. It is trained using a variant of the transformer architecture and is trained on a dataset of billions of words from the internet. The model is fine-tuned on specific tasks using a smaller dataset. This is done by adjusting the model’s parameters to optimize its performance on the specific task. The fine-tuning process is typically done using supervised learning, where the model is provided with input-output pairs and the goal is to learn to map inputs to corresponding outputs.”

CJ: “What time frame does the training dataset span?”

ChatGPT: “The dataset used to train ChatGPT is a collection of text from the internet, it’s not specified the exact years of the data, but it’s likely that it includes a variety of text from different years, as the model is trained on a diverse range of internet text to make it generalizable to different types of language tasks. However, it’s important to note that the training data cut-off date is 2021, so it won’t include any text written after this date.”

CJ: “That means ChatGPT cannot answer questions about the current news. Is that right?”

ChatGPT: “That is correct. As a pre-trained language model, ChatGPT is based on a snapshot of the internet as it existed up to a certain date (2021). So it will not have information about events or news that have occurred after that date. However, it can still provide general information or context about a topic that may be helpful. Additionally, there are methods for fine-tuning pre-trained models on new data which can help ChatGPT to adapt to new information.”

CJ: “But for how long will ChatGPT rely on historic data?”

ChatGPT: “It depends on the specific use-case and how frequently the model is fine-tuned on new data. For some applications, it may be sufficient to use a pre-trained model like ChatGPT as-is, while for others, it may be necessary to fine-tune the model on new data more frequently to ensure it stays up-to-date. In general, the more dynamic and fast-paced the domain, the more frequently the model should be fine-tuned on new data. It’s also worth noting that OpenAI may release updated versions of ChatGPT with newer data.”

CJ: “Will there be a time when we will ask ChatGPT about daily news?”

ChatGPT: “It is possible that in the future, ChatGPT or similar models could be fine-tuned on a more frequent basis to include information about daily news. However, this would require a significant amount of data and computational resources to keep the model updated with the most current information. Additionally, it would be important to ensure that the model can accurately understand and respond to questions about news events, which would require additional fine-tuning and testing.”

CJ: “Humans have a lot of biases, and the internet represents mostly human behaviour. Does that mean that ChatGPT and other Internet data trained language models will replicate such biases?”

ChatGPT: “Yes, it is possible that language models trained on internet data, such as ChatGPT, may replicate biases that are present in the data. This is because the model is trained on text written by humans, and the data can reflect the biases and stereotypes that exist in society. There are several types of biases that can appear in the data, for example, gender bias, racial bias, and cultural bias. These biases can manifest in the model’s output in various ways, such as by generating text that reinforces stereotypes or by providing different responses to similar questions based on the gender or race of the person asking the question. This is an active area of research and there are several ongoing efforts to mitigate bias in language models. One approach is to use techniques to identify and remove biased examples from the training data, while another is to use techniques to debias the model during or after training. It’s important to note that OpenAI is aware of this issue and is actively working to mitigate the bias in their models, and also providing resources for users to understand and mitigate the bias in their own models.”

CJ: “How could that problem be fixed?”

ChatGPT: “There are several approaches that can be used to mitigate bias in language models, including the following: 1. Data preprocessing: One approach is to identify and remove biased examples from the training data. This can be done by using techniques such as bias detection algorithms and then removing or correcting the examples that are found to be biased. 2. Fine-tuning on diverse data: Another approach is to fine-tune the model on a more diverse dataset, which can help to reduce bias by exposing the model to a wider range of perspectives and experiences. 3. Regularization techniques: During the training process, regularization techniques can be used to prevent the model from overfitting to the biased examples in the data. 4. Adversarial debiasing: Another approach is to use adversarial training to debias the model. The idea is to add a bias classifier to the model and then optimize the model and the classifier together so that the model’s predictions are less biased. 5. Post-processing: Finally, post-processing techniques can be used to debias the model’s output. This can include techniques such as rephrasing or rewriting the model’s output to remove any biased language. It’s important to note that there is no one-size-fits-all solution for mitigating bias in language models, and different approaches may be more effective for different types of biases and in different contexts. Also, It’s worth noting that the process of debiasing is an ongoing task, as new biases may be introduced to the model during its usage, and it’s an area of active research, new methods and techniques may be developed to handle this problem better.”

CJ: “Thank you for this interview.”

ChatGPT: “You’re welcome! If you have any other questions or need further clarification, feel free to ask.”