[HN Gopher] Scaffolded LLMs as natural language computers
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Scaffolded LLMs as natural language computers
Author : veerd
Score : 67 points
Date : 2023-04-12 19:00 UTC (4 hours ago)
(HTM) web link (www.beren.io)
(TXT) w3m dump (www.beren.io)
| [deleted]
| highduc wrote:
| What 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.
| karmasimida wrote:
| It has memory and it has built in interpreter, and it is even its
| own runtime
|
| Pretty incredible
| killthebuddha wrote:
| People have already started writing the languages. Here's an
| example that I think is really neat:
| https://github.com/jbrukh/gpt-jargon.
| galaxyLogic wrote:
| This is what I've been wondering what happens when you replace
| the human in the human-chatbot loop with another chatbot? I'm
| sure somebody must have tried it?
|
| Those 2 chatbots could be of different models. Would they then
| teach each other something new?
| tmaly wrote:
| I 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]
| lsy wrote:
| I 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.
| catchnear4321 wrote:
| After a few times, you could ask it to reduce its purpose to a
| couple of lines of code. Test the code, verify it. Maybe even
| deploy it.
|
| That's more expensive than writing a couple of lines of code,
| more than deploying it,
|
| Sufficiently standardized, it will be significantly less
| expensive than paying someone to write a couple of lines of
| code, test it, deploy it, etc.
|
| Is this better than just writing a couple of lines of code?
| That's a different question. At scale, this can absolutely be
| cheaper. Eventually.
| vidarh wrote:
| The big value is certainly in the less constrained stuff. Today
| I wanted to experiment with a hobby project, so I wrote a page
| of what I want to achieve. I then presented it to GPT4 as a
| "spec" and told it to act as a software architect and give me
| suggestions and ask for clarification. It gave me mostly good
| suggestions, asked a few good questions, and gave some feedback
| I disagreed with. I updated the spec to incorporate the good
| bits, clarified and firmed up the wording around the choices
| where it had made suggestions I disagreed with, and asked it to
| give another round of feedback. I turned around the draft 3
| times, and it added value every time.
|
| I could have done that with a person, but then I'd need someone
| who was available then and there that I wasn't taking away from
| other stuff.
|
| To tie that to your points: This process was exactly an
| exercise in nailing down details that were missing because the
| _short_ prose version was leaving huge gaps.
|
| I intend to need a back and forth of filling in detail each
| step toward an implementation for that reason.
|
| And I think this kind of tooling need to be built with that in
| mind: Write roughly what you need _unless_ you already know how
| you want to express it in code. Ask for clarifications or a
| proposed plan. Iterate. Maybe with tooling giving examples of
| where it would head if prompted to fill in more detail.
|
| I sure as hell won't trust it to just blindly do a task for me
| from a brief problem statement at this point.
|
| Just as I wouldn't for most non-trivial tasks with most
| humans...
| qup wrote:
| > Natural language is a pretty fantastically bad interface for
| specifying unambiguous, repeatable, and reliable tasks
|
| I agree. I think people are fooled by the implicit knowledge
| that most humans have. They make decisions about the details,
| where in programming you have to tend to each detail. That's
| roughly equivalent effort in natural language.
|
| For some problems, probably harder in natural language.
|
| I still think it's a great advancement. It opens programming to
| the masses, and the LLM can probably teach you the things you
| need to know to advance your competency.
| galaxyLogic wrote:
| AI must learn to understand code, not just copy it
| manmal wrote:
| I think developing often used functions as plugins/task
| primitives as native code (i.e. tools that the LLM can use)
| would be a logical next step - as you expressed, I think.
|
| > 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
|
| I think the problem is that natively written code will always
| suck at the planning part. An LLM can use the tools available
| to it (eg a web browser or Wolfram) in infinite ways, and
| hopefully in a way that will advance the task at hand. Maybe
| the natively written task primitives can become really big (eg
| a fully automatic web scraper) - great, now the LLM can gather
| info for its plans even faster.
| furyofantares wrote:
| It's pretty often that people tell a programmer what they want
| in not-that-much natural language and then the programmer makes
| it happen. You do have to verify the programmer's work and
| often provide some corrections that may have been the result of
| unrecognized ambiguity, but it still ends up being vastly less
| natural language sent to the programmer than the amount of
| precise language the programmer ultimately specifies.
| mcemilg wrote:
| The 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.
| sharemywin wrote:
| I remember reading this book a long time ago and thought it had
| some pretty interesting concepts around agents:
|
| https://aima.cs.berkeley.edu/
|
| this was another one:
|
| https://www.amazon.com/Artificial-Intelligence-3rd-Winston/d...
| zan2434 wrote:
| This was an inspiring read! Reminds me of Simon Willison's
| analogy of LLMs to "calculators for words" but this author takes
| the idea even further. I agree the analogy points to foundation
| model companies like OpenAI and Anthropic having the most revenue
| but not the highest margins. Who will the Apple / Microsoft /
| Google of this new wave be? Who can take this raw technology and
| actually make it usable by all? "An LLM in every home"
| jackblemming wrote:
| >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.
| richardfeynman wrote:
| I agree that there are still differences between humans and AI,
| and vision is one of them. Humans also remember across
| conversations, ChatGPT doesn't. We have longer context windows.
| We think in our downtime, unprompted. We take input from 5
| senses, today's chatGPT only takes input from text.
|
| But--and this is a big but--the set of things that people can
| do better than computers has shrunk significantly over the past
| five months. Today, thanks to GPT4, AI can get a B in a Quantum
| Computing class, generate engaging stories, know that the color
| yellow is closer to orange than blue (despite never having seen
| color!), answer emotionally laden questions with the sort of
| facility that IMO is better than most humans, write code,
| rhyme, and much more. All of this stuff was unthinkable before.
| I personally thought it would be centuries until this stuff was
| possible. I was very wrong.
|
| Several developments in Deep Learning, like the Transformers
| paper (https://arxiv.org/abs/1706.03762), set off this growth,
| as did big data and increased computing power. The insight of
| particular humans, like Ilya Sutskever, played a role as well.
| But taken together, I actually don't understand how one can
| argue that we _aren 't_ at the beginning of a massive
| exponential.
|
| Of course there are things humans can still do better than AI,
| but the number of things is shrinking rapidly, while the number
| of things computers can do better than humans is growing
| rapidly.
|
| I argue that we are indeed at the beginning of an exponential,
| and we'll see both new classes of products and faster
| development time.
| jonplackett wrote:
| This is a long but worthwhile read.
|
| Opened my mind to what is to come and how it might happen
| manmal wrote:
| As a software developer, this article has been able to give me a
| glimmer of hope that my skills won't be fully obsolete once LLMs
| mature. The high level instructions (author calls them
| ,,programs" even) will require highly structured thinking,
| translating business goals into actionable slices.
|
| I've played with AutoGPT today, and, while the results were
| underwhelming (once it crashed, and once it got stuck in an
| infinite loop because it wrongly requested a website critical for
| the task) - it was an experience very similar to my first
| attempts at learning C. I tried to tell the system what I want it
| to do, and it mostly really followed my instructions. If (when?)
| all the components have improved in reliability and speed, this
| will become an insanely powerful way of working. A la ,,Make a
| website from this PDF with nextjs and deploy it to netlify". Not
| very unlike ,,Read this file from disk and parse CSV rows from
| it" as we are doing now, as devs working with high level
| programming languages.
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