There was always a loop-closing method on the table with Wikipedia. Where is the loop-closing method to bullshit on LLMs not being efficient lie generators?
What is loop closing? (Google didn't help, my results were SLAM and DIY). It sounds like citogenesis which is the opposite of the rest of your comment?
In this context, "loop closing" likely refers to a method of addressing or resolving issues or gaps in a process or system. The speaker seems to be expressing frustration about a perceived lack of a method to counter misinformation or falsehoods when it comes to Language Model (LLMs) efficiency."""
So, same inference I made from the context (alone it sounds like citogenesis but the context suggests the opposite), and same lack of specific detail.
Me: why did the commenter use the term “loop-closing”?
ChatGPT: The commenter used the term "loop-closing" to emphasize the concept of a feedback mechanism that corrects or validates information. In systems theory and engineering, a "closed loop" system is one where the output is continually fed back into the system as input for self-correction and optimization. This term is metaphorically applied here to describe a process where information is constantly reviewed and corrected, forming a self-regulating cycle.
In the context of Wikipedia, this loop-closing happens through community edits and oversight, where incorrect information is often quickly corrected by other users. The commenter is highlighting the absence of a similar, effective mechanism in Large Language Models (LLMs) to automatically correct or verify the information they produce. This comparison underscores a concern about the potential for LLMs to disseminate incorrect or misleading information without an inherent system for checking and correcting it. The choice of "loop-closing" as a term is to draw attention to this systematic difference in information validation between platforms like Wikipedia and LLMs.
As it happens, I just watched some of the OpenAI developer day videos, and they do talk about connecting the models with more traditional search engines/databases/API calls to a similar end.
