111–120 of 385 posts
I build an Agentic AI that leverages #6 and #7 at the end of the article as well as techniques not yet published. It tackles hallucination relative not to the world at large but to the facts, entities and causal relationships contained in a document (which is really bad reasoning if we assume LLMs are "reasoning" to begin with) It also tackles cross-reasoning with very large token distance. https://www.youtube.com/wa…
I build an Agentic AI that leverages #6 and #7 at the end of the article as well as techniques not yet published. It tackles hallucination relative not to the world at large but to the facts, entities and causal relationships contained in a document (which is really bad reasoning if we assume LLMs are "reasoning" to begin with) It also tackles cross-reasoning with very large token distance. https://www.youtube.com/wa…
Wow. Please do a show HN. Is source available? I would love to play with this
I have been trying to generate some text recently using the ChatGPT API. No matter how I word “Include any interesting facts or anecdotes without commenting on the fact being interesting” it ALWAYS starts out “One interesting fact about” or similar phrasing. I have honestly spent multiple hours trying to word the prompt so it will stop including introductory phrases and just include the fact straight. I have gone so…
I just tried this locally with llama3-8b and it handled it fine. Claude 3 Sonnet passes your test too, in case you don't have the hardware for a local model. You might want to consider moving on from ChatGPT since their models have been RLHF'd to hell in the name of "safety" and are falling behind in many ways as a result. --- transcript: $ ollama run llama3:8b >>> tell me an interesting fact about etymology Here's o…
I build an Agentic AI that leverages #6 and #7 at the end of the article as well as techniques not yet published. It tackles hallucination relative not to the world at large but to the facts, entities and causal relationships contained in a document (which is really bad reasoning if we assume LLMs are "reasoning" to begin with) It also tackles cross-reasoning with very large token distance. https://www.youtube.com/wa…
Earlier quoted context omitted.
Neural nets can approximate any function. A large enough llm with memory is turning complete. So theoretically I don’t think there is anything they can never do.
> Neural nets can approximate any function. Common misunderstanding of the universal approximation theorem. Consider this: can an mlp approximate a sine wave? > A large enough llm with memory is turning complete. With (a lot of) chain of thought it could be. Read the paper, and its references.
Earlier quoted context omitted.
Wow. Please do a show HN. Is source available? I would love to play with this
Thanks. This is just in the labs stage, but moving closer to releasing it, exactly so that you can play with it! I have one angel investor involved in supporting this and it's intended for commercial applications in the para legal space, initially (controlled, structured environment) But you just gave me the motivation to "put it out there" so people can just play with it. It'll take a bit of time, but I will do a Sh…
Earlier quoted context omitted.
“Ten” is a word, “10” are digits. I’m not a native English speaker, how would you write it? FWIW the LLMs get it right many times, but fail other times.
I couldn't understand the original wording either, but after reading one of the sibling comments that explains it, it suddenly made sense. I think you left out a few words that most English writers would include. So instead of: > "ensure that numbers from one to ten as written as words and numbers greater ten as digits in the given text", something like the following might be better for most people: > "ensure that th…
Fantastic essay. Highly recommended! 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…
Fantastic essay. Highly recommended! 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…
In complete seriousness, can anyone can explain why LLMs are good at some tasks?
Fantastic essay. Highly recommended! 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…
I thought we did know for things like playing Wordle, that its because they deal with words as sequence of tokens that correspond to whole words not sequences of letters, so a game that involves dealing with sequences of letters constrained to those that are valid words doesn’t match the way they process information?
> 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.
But providing examples with different, contextually-appropriate sets of reasoning steps results can enable the model to choose its own, more-or-less appropriate, set of reasoning steps for particular questions not matching the examples.
> It’s unclear if better prompting and bigger models using existing attention mechanisms can achieve AGI.
Since there is no objective definition of AGI or test for it, there’s no basis for any meaningful speculation on what can or cannot achieve it; discussions about it are quasi-religious, not scientific.