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The article should be titled " What can LLM never do, yet". By definition, Large Language Models would keep growing larger and larger, to be trained on faster and more advanced hardware, and certain points like "completing complex chains of logical reasoning" tasks, would be just a time hurdle. Only time will tell.
I don't think it should. It's more interesting to know what LLMs will _never_ be able to do (if anything).
The article should be titled " What can LLM never do, yet". By definition, Large Language Models would keep growing larger and larger, to be trained on faster and more advanced hardware, and certain points like "completing complex chains of logical reasoning" tasks, would be just a time hurdle. Only time will tell.
Guarantee an output will be consistent every time.
I agree with all key points:
* There are problems that are easy for human beings but hard for current LLMs (and maybe impossible for them; no one knows). Examples include playing Wordle and predicting cellular automata (including Turing-complete ones like Rule 110). We don't fully understand why current LLMs are bad at these tasks.
* Providing an LLM with examples and step-by-step instructions in a prompt means the user is figuring out the "reasoning steps" and handing them to the LLM, instead of the LLM figuring them out by itself. We have "reasoning machines" that are intelligent but seem to be hitting fundamental limits we don't understand.
* It's unclear if better prompting and bigger models using existing attention mechanisms can achieve AGI. As a model of computation, attention is very rigid, whereas human brains are always undergoing synaptic plasticity. There may be a more flexible architecture capable of AGI, but we don't know it yet.
* For now, using current AI models requires carefully constructing long prompts with right and wrong answers for computational problems, priming the model to reply appropriately, and applying lots of external guardrails (e.g., LLMs acting as agents that review and vote on the answers of other LLMs).
* Attention seems to suffer from "goal drift," making reliability hard without all that external scaffolding.
Go read the whole thing.
The article should be titled " What can LLM never do, yet". By definition, Large Language Models would keep growing larger and larger, to be trained on faster and more advanced hardware, and certain points like "completing complex chains of logical reasoning" tasks, would be just a time hurdle. Only time will tell.
The article should be titled " What can LLM never do, yet". By definition, Large Language Models would keep growing larger and larger, to be trained on faster and more advanced hardware, and certain points like "completing complex chains of logical reasoning" tasks, would be just a time hurdle. Only time will tell.
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LLMs are as friendly to society as nuts and bolts. It's up to those who wield it to be moral.
As amazing as they are, they still have many limitations.
I’ve been working with ChatGPT and Gemini to apply simple rules like the one above and I got so frustrated.
Third paragraph. The entire article is based on the premise LLMs are supposed to reason, which is wrong. They don't, they're tools to generate text.
There’s many things they can’t do. Even a simple rule like “ensure that numbers from one to ten are written as words and numbers greater ten as digits in the given text” fails for me for so many examples even if it works for many others; few shot, chain of thought, many versions of the prompt, it doesn’t matter. Sometimes LLMs will even change the number to something else, even with temp set to 0. And then there’s th…
I can’t fault llms for not knowing what to do here because I, a human, have no idea what on earth this means.