21–30 of 192 posts
The next step in the research should be ablation of these time and space neurons to see how it affects accuracy on space/time completions which would help rule out the memorization of linear probes.
A lot of HNers were so adamant that the LLMs understand absolutely nothing and that these models are just predicting the next most likely word. I think with this paper it becomes clear that the adamant denial was just human bias talking. LLMs crossed a certain line here. You would think people would be amazed or in awe or react in fear at technological break through a and they often are. The weird part for LLMs was t…
My only experience so far is with chatgpt, and I am not an expert in AI, or even just in LLMs. With those disclaimers, my interactions inform my opinion that the LLM behind chatgpt has no internal world model. It shows no understanding of basic facts and makes very silly mistakes very easily. I have my bias, like anyone else, but in the case of AI in particular, I should say that I don't think there's anything especi…
1. Did you use GPT-3.5 (free version) or GPT-4 (paid one) to form the judgment? Their performances on harder tasks differ significantly as shown in https://openai.com/research/gpt-4.
2. Have you tried adding "Please think step by step" to some harder requests? This simple phrase gets most current LLMs to perform significantly better. It's a bit like asking students to show their work, which forces them to think more clearly.
Current LLMs, without additional mechanisms, tend to be perform like a drunk or sleepy human, i.e. using mostly intuition or System-1 (as defined in "Thinking Fast and Slow"). The prompt such as "think step by step" asks it to think more in the System-2 style. (There are other techniques which get them to perform even better still.)
I think of current LLMs as a very-well-read, but often sleepy intern who needs strict instructions, feedback, and sometimes extra training if you want them to perform well.
The Twitter thread is worth looking at. Very Fascinating https://twitter.com/wesg52/status/1709551516577902782?t=3b2F...
A lot of HNers were so adamant that the LLMs understand absolutely nothing and that these models are just predicting the next most likely word. I think with this paper it becomes clear that the adamant denial was just human bias talking. LLMs crossed a certain line here. You would think people would be amazed or in awe or react in fear at technological break through a and they often are. The weird part for LLMs was t…
Indeed. I was offering examples of how you could bootstrap cognitive processes at the beginning of the year and people were just sticking their fingers in their ears. A lot of people exercise their intellectual, economic, or political freedoms by rejecting others' efforts to gain the same things.
The Twitter thread is worth looking at. Very Fascinating https://twitter.com/wesg52/status/1709551516577902782?t=3b2F...
A lot of HNers were so adamant that the LLMs understand absolutely nothing and that these models are just predicting the next most likely word. I think with this paper it becomes clear that the adamant denial was just human bias talking. LLMs crossed a certain line here. You would think people would be amazed or in awe or react in fear at technological break through a and they often are. The weird part for LLMs was t…
This seems like the model found on its own another dimension to segregate data in deeper layers by generalizing and using context when learning. Its cool it can do that, but it still to me seems like drawing a best fit line just with higher dimensions.
I'm sure someone will correct me if they think I am way off.
A lot of HNers were so adamant that the LLMs understand absolutely nothing and that these models are just predicting the next most likely word. I think with this paper it becomes clear that the adamant denial was just human bias talking. LLMs crossed a certain line here. You would think people would be amazed or in awe or react in fear at technological break through a and they often are. The weird part for LLMs was t…
This is a preprint that was just uploaded to arxiv two days ago. Don't be hasty and assume that it settles any matter at all. Many such claims have been made before and many counter-claims also. There is still a lively debate on the subject and it will be some time before there is agreement. More generally, any scholarly article is a claim, and should never be read as automatically true. That's something to keep in m…
A lot of HNers were so adamant that the LLMs understand absolutely nothing and that these models are just predicting the next most likely word. I think with this paper it becomes clear that the adamant denial was just human bias talking. LLMs crossed a certain line here. You would think people would be amazed or in awe or react in fear at technological break through a and they often are. The weird part for LLMs was t…
But we don't have the output to analyze aside from our language and assumption of shared experience
So for many people they don't really distinguish between things like "reasoning", "self-aware", "conscious", "alive", "sentient", "intelligent", "has world model". They also don't distinguish between different types or varying levels of cognitive abilities.
It seems clear that high functioning LLMs must have some type of world model. But that doesn't mean it's necessarily exactly the same type of highly grounded model that a human would have, especially if it was trained on only text. It might be less rich or different but still quite useful.
Another example, LLMs clearly don't have the same type of fast adaptation in a realtime 3d environment that animals have. (That's not to say that they can't mimic it in some rough ways).
But if you don't really break all of this stuff down carefully in your head then it can be hard to accept that LLMs are doing anything interesting. Because in that worldview, it's all the same thing, so they have to give the LLM all of the other characteristics at the same time.