Can today’s artificial intelligence systems be used to train superior artificial intelligence systems of tomorrow? Can AI write better AI that writes better AI leading to a potentially god-like general artificial intelligence? In a recent insightful paper written by collaborators from Oxford, Cambridge and other prestigious institutions, the verdict is in for large language models (LLMs) like ChatGPT. Repeatedly using the output of one LLM to train another results in what the authors call model collapse. If only the words from the original LLM #0 are used to train LLM #1 and only the output of LLM #1 is used to train LLM #2, and so on, the AI eventually suffers model collapse and becomes a blubbering idiot. The emergence of superintelligence by this repeated process never happens. The opposite does. The result, rather, is super idiocy.
Blubbering idiocy can be avoided in LLMs by transfusion of fresh information from the creative minds of humans. The paper’s authors conclude that “to avoid model collapse, access to genuine human-generated content is essential.”
The paper’s authors illustrate model collapse using an LLM dubbed OPT-125m (Open Pre-trained Transformer) initially made available from Meta (a.k.a. Facebook). In the following example from the paper, each new generation is trained on data produced by the previous generation. The same input prompt is used for each generation. Here’s the result with Gen 0 being the response from the original LLM.
- Input: some started before 1360 — was typically accomplished by a master mason and a small team of itinerant masons, supplemented by local parish labourers, according to Poyntz Wright. But other authors reject this model, suggesting instead that leading architects designed the parish church towers based on early examples of Perpendicular
- Gen 0: Revival architecture such as St. John’s Cathedral in London. The earliest surviving example of Perpendicular Revival architecture is found in the 18th @-@ century Church of Our Lady of Guernsey, which dates from the late 19th century. There are two types of perpendicular churches : those…
- Gen 1: architecture such as St. Peter’s Basilica in Rome or St. Peter’s Basilica in Buenos Aires. There is no evidence that any of these buildings were built during the reign of Pope Innocent III, but it is possible that they may have been built during the reign of his successor, Pope Innocent …
- Gen 7: architecture in England. In an interview with The New York Times, Wright said : ” I don ’t think there is anything wrong with me being able to do what I want to do. It just doesn ’t work for me. ” He added : ” I don’t know if you can call it funny, …
- Gen 9: architecture. In addition to being home to some of the world’s largest populations of black @-@ tailed jackrabbits, white @-@ tailed jackrabbits, blue @-@ tailed jackrabbits, red @-@ tailed jackrabbits, yellow @ …
Indeed, Gen 9 sounds like a blubbering idiot. The response has nothing to do with the LLM prompt.
The authors note that model collapse will occur in other uses of generative AI. Consider image-generating AI trained on pictures scraped from the web. Suppose images thus generated were used to train another image generator, and so on. After a few generations, the generated images will not be very interesting. To our knowledge, no such multiple generation examples exist — but this would be fun to look at.
What about AI-generated music? The authors say that “one can also consider what happens when music created by human composers and played by human musicians trains models whose output trains other models.” Model collapse will occur here also.
How might this impact the World Wide Web of the future? LLM systems can get fresh text by going to the web for new material. But what happens if someday much of the content of the web is written by generative AI? The web scrapings won’t be generated by creative humans and the newly generated material will suffer from early signs of model collapse. Unchecked, the web might contain a lot of content that resembles a blubbering idiot.
LLMs like ChatGPT produce spectacular results. Under the hood, LLMs impressively manipulate relational syntax to do their magic. They learn arrangements of words and phrases to create well-formed documents. Humans on the other hand are motivated by semantics — the meaning of words and phrases. We pay attention to syntax, but the meaning of the message is of primary importance. Model collapse illustrates that freshly generated meaning from creative humans is required to advance generative AI to higher levels of performance.