cm0002Anthropic has developed an AI 'brain scanner' to understand how LLMs work and it turns out the reason why chatbots are terrible at simple math and hallucinate is weirder than you thought
Captain Poofterthis is one of the most interesting things about Llms that i have ever read
That bit about how it turns out they aren’t actually just predicting the next word is crazy and kinda blows the whole “It’s just a fancy text auto-complete” argument out of the water IMO
VoroxpeteIt really doesn’t. You’re just describing the “fancy” part of “fancy autocomplete.” No one was ever really suggesting that they only predict the next word. If that was the case they would just be autocomplete, nothing fancy about it.
What’s being conveyed by “fancy autocomplete” is that these models ultimately operate by combining the most statistically likely elements of their dataset, with some application of random noise. More noise creates more “creative” (meaning more random, less probable) outputs. They do not actually “think” as we understand thought. This can clearly be seen in the examples given in the article, especially to do with math. The model is throwing together elements that are statistically proximate to the prompt. It’s not actually applying a structured, logical method the way humans can be taught to.
Unfortunately, these articles are often written by people who don’t know enough to realize they’re missing important nuances.
It also doesn’t help that the AI companies deliberately use language to make their models seem more human-like and cogent. Saying that the model e.g. “thinks” in “conceptual spaces” is misleading imo. It abuses our innate tendency to anthropomorphize, which I guess is very fitting for a company with that name.
On this point I can highly recommend this open access and even language-wise accessible article: link.springer.com/article/…/s10676-024-09775-5 (the authors also appear on an episode of the Better Offline podcast)
ChatGPT is bullshit - Ethics and Information Technology
Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.
I can’t contemplate whether LLMs think until someone tells me what it means to think. It’s too easy to rely on understanding the meaning of that word only through its typical use with other words.