LLMs _can_ think top-to-bottom but only if you make them think about concrete symbol based problems. Like this one: https://chatgpt.com/s/t_692d55a38e2c8191a942ef2689eb4f5a
The prompt I used was "write out the character 'R' in ascii art using exactly 62 # for the R and 91 Q characters to surround it with"
Here it has a top down goal of keeping the exact amount of #'s and Q's and it does keep it in the output. The purpose of this is to make it produce the asciii art in a step by step manner instead of fetching a premade ascii art from training data.
What it does not reason well about always are abstract problems like the doctor example in the post.
The real key for reasoning IMO is the ability to decompose the text into a set of components, then apply world model knowledge to those components, then having the ability to manipulate those components based on what they represent.
Humans have an associative memory so when we read a word like "doctor", our brain gathers the world knowledge about that word automatically. It's kind of hard to tell exactly what world knowledge the LLM has vs doesn't have, but it seems like it's doing some kind of segmentation of words, sentences and paragraphs based on the likelihood of those patterns in the training data, and then it can do _some_ manipulation on those patterns based on other likelihood of those patterns.
Like for example if there is a lot of text talking about what a doctor is, then that produces a probability distribution about what a doctor is, which it then can use in other prompts relating to doctors. But I have seen this fail before as all of this knowledge is not combined into one world model but rather purely based on the prompt and the probabilities associated with that prompt. It can contradict itself in other words.
Here it has a top down goal of keeping the exact amount of #'s and Q's and it does keep it in the output. The purpose of this is to make it produce the asciii art in a step by step manner instead of fetching a premade ascii art from training data.
What it does not reason well about always are abstract problems like the doctor example in the post. The real key for reasoning IMO is the ability to decompose the text into a set of components, then apply world model knowledge to those components, then having the ability to manipulate those components based on what they represent.
Humans have an associative memory so when we read a word like "doctor", our brain gathers the world knowledge about that word automatically. It's kind of hard to tell exactly what world knowledge the LLM has vs doesn't have, but it seems like it's doing some kind of segmentation of words, sentences and paragraphs based on the likelihood of those patterns in the training data, and then it can do _some_ manipulation on those patterns based on other likelihood of those patterns. Like for example if there is a lot of text talking about what a doctor is, then that produces a probability distribution about what a doctor is, which it then can use in other prompts relating to doctors. But I have seen this fail before as all of this knowledge is not combined into one world model but rather purely based on the prompt and the probabilities associated with that prompt. It can contradict itself in other words.