Scaffolded LLMs as natural language computers
1–10 of 26 posts
Re: Scaffolded LLMs as natural language computers
#2[deleted]
Re: Scaffolded LLMs as natural language computers
#3I am interested to see how the concept longterm memory is developed with LLMs. It seems very slow to use fine tuning for this process.
On the topic of summarizing and determinism, I wonder if an intermediate bytecode loke language or structured subset of the English language could improve the outcome across models.
Re: Scaffolded LLMs as natural language computers
#4This is a long but worthwhile read.
Opened my mind to what is to come and how it might happen
Re: Scaffolded LLMs as natural language computers
#5What would be good approaches to also implement a personality layer, that can be more complex. Something like an effect box over the LLM that contains the info.
Re: Scaffolded LLMs as natural language computers
#6I am interested to see how the concept longterm memory is developed with LLMs. It seems very slow to use fine tuning for this process. On the topic of summarizing and determinism, I wonder if an intermediate bytecode loke language or structured subset of the English language could improve the outcome across models.
[deleted]
Re: Scaffolded LLMs as natural language computers
#7It has memory and it has built in interpreter, and it is even its own runtime
Pretty incredible
Re: Scaffolded LLMs as natural language computers
#8>My prediction here is that exponential improvements continue at least for the new few years and likely beyond.
GPT-3 to GPT-4 was an exponential improvement? Progress is not usually exponential. Your phone now isn’t x10 better than it was a few years ago. Progress in AI is a huge jump, then refinement of that jump, then stagnation until the next big jump is discovered. Look at CNNs dominating image classification competitions out of nowhere, then they got refined, then they kind of stagnated and didn’t get much % accuracy better in whatever benchmark was used, in fact I think humans are still way better at many vision tasks and it’s been over a decade of research now since CNNs jumped on the scene as the hot thing. I don’t know why people refuse to understand or see this, but it’s tiring constantly hearing people pretend everything is exponential and AGI is two years away when AI hasn’t even beaten humans at some pretty trivial vision benchmarks.
Re: Scaffolded LLMs as natural language computers
#9I think the issue is that the sentence "Many tasks cannot be specified easily and precisely in computer code but can be described in a sentence or two of natural language" is not, in fact, true. Natural language is a pretty fantastically bad interface for specifying unambiguous, repeatable, and reliable tasks, which is why most technical advancement has involved the introduction of expressive notations that clarify and constrain problems in a way natural language can't. And the bulk of this article predicts (I think correctly!) that if LLMs are to be used for more automation, they have to progress towards some form of "semantic codes", "task primitives", "abstractions", ie. notation. "Prompt engineering" is a form of reaching for notation, although I would say at this point it is more like "prompt guessing".
However this prompts the question of why we are striving to create massive natural language models (with all the disadvantages of natural language) that we will then heavily constrain to perform tasks that can be performed by traditional computers with order-of-magnitude greater efficiency and reliability? Most of these "chaining" libraries are already engaged in asking the LLM to pretty-please output a standardized blob format that can be read by a Python harness, where we use the LLM to identify that something is e.g. an equation, and then pack it off to Wolfram or something. It seems like if you want to do this more than a few times, it's better to write a couple lines of code to do it much more cheaply.
Re: Scaffolded LLMs as natural language computers
#10The most impressive aspect of ChatGPT for me is its ability to understand natural language. It's remarkable how it can comprehend corrupted text and discern what you're trying to convey. I believe that large language models will be utilized as natural language processors in the near future. However, unfortunately, alternatives like LLAMA, ALPACA, or Open Assistant are not yet on par with GPT-4. I don't think they're sufficient to be used as Natural Language Processing Units. We can't build a computer that relies on an API powered by a closed company.