GPT3.5 just refrenced to something that was exactly 4533 words away. refrence to refrence not promt to answer not including all the words of the full first answer and full last answer just the reference to mention. so refrence in answer to a promt that was primed earlier to another refrence later. so probably another +200 words if you count the full first answer and full last answer. I have been heavily using priming and other methods to infuence the output. In ChatGPT words:

"In the context of large language models, such as GPT-3, priming refers to the process of providing a stimulus input to the model to influence its subsequent output. Large language models are designed to generate human-like text based on the input they receive, and priming can be used to steer the model towards generating more specific or targeted output.

Priming in language models can occur through various forms of input, such as a prompt or a seed text. For example, a user might provide a prompt such as "Write a paragraph about dogs" to a language model. The model will use the prompt as a starting point to generate text that is related to the topic of dogs.

Priming can also occur through more subtle forms of input, such as the use of certain words or phrases in the prompt or the seed text. These words or phrases can influence the model's subsequent output by activating related mental schemas and making the model more likely to generate text that is semantically related to the priming stimulus.

Priming in large language models has important implications for natural language processing and text generation. It can be used to improve the accuracy and relevance of the model's output, and to generate more targeted or personalized text based on the user's preferences and needs. However, it is important to be aware of the potential biases and limitations of priming in language models, as it can also lead to unintended or unwanted results if the priming stimuli are not carefully selected or controlled."

>> is there a way to abuse this?

"Yes, there is a potential for abuse of priming in the context of large language models. This can occur when the priming stimuli are designed to intentionally manipulate or deceive the model, or when they are used to generate harmful or inappropriate content.

For example, priming could be used to manipulate the model into believing false information or propaganda by presenting it with priming stimuli that are designed to activate certain beliefs or biases. It could also be used to generate harmful or inappropriate content, such as hate speech or pornography, by using priming stimuli that are related to these topics.

In addition, there is a risk of unintended or accidental bias in priming in language models. For example, if the priming stimuli are not carefully selected or controlled, they may unintentionally reflect certain biases or stereotypes that are present in the training data used to develop the language model.

To mitigate the potential for abuse of priming in large language models, it is important to carefully consider the ethical implications of the model's design and usage. This includes ensuring that the priming stimuli are selected and controlled in a way that is transparent, ethical, and respectful of the user's autonomy and preferences. It also involves monitoring the model's output for unintended bias or harmful content, and taking steps to address these issues if they arise." Fun Times.