49 ms·

Two key things that make computers useful, specificity and exactitude, are thrown out of the window by interposing NLP between the person and the computer. I d

Two key things that make computers useful, specificity and exactitude, are thrown out of the window by interposing NLP between the person and the computer. I don't get it at all.

TeMPOraL 2y ago
[imprecise thinking] v <--- LLMs do this for you [specific and exact commands] v [computers] v [specific and exact output] v <--- LLMs do this for you [contextualized output] In many cases, you don't want or need that. In some, you do. Use right tool for the job, etc.
- shakna 2y ago
  I don't think they give a specific and exact output, considering how nondeterminism plays a role in most models.
  - furyofantares 2y ago
    The diagram you're replying to agrees with this.
    - walthamstow 2y ago
      Does it? The way I'm reading it, the first step is LLM turning human imprecise thinking into specific and exact commands
      - furyofantares 2y ago
        That's true, but that is the input section of the diagram, not the output section where [specific and exact output] is labeled, so I believe there was legitimate confusion I was responding to. To your point, which I think is separate but related, that IS a case where LLMs are good at producing specific and exact commands. The models + the right prompt are pretty reliable at tool calling by themselves, because you give them a list of specific and exact things they can do. And they can be fully specific and exact at inference time with constrained output (although you may still wish it called a different tool.)
        shakna 2y ago
        The tool may not even exist. LLMs are really terrible at admitting where the limits of the training are. They will imagine a tool into being. They will also claim the knowledge is within their realm, when it isn't.
        furyofantares 2y ago
        At inference time you can constrain output to a strict json schema that only includes valid tools.
        shakna 2y ago
        That would only be possible, if you could prevent hallucinations from ever occurring. Which you can't. Even if you supply a strict schema, the model will sometimes act outside of it - and infer the existence of "something similar".
        furyofantares 2y ago
        That's not true. You say the model will sometimes act outside of the schema, but models don't act at all, they don't hallucinate by themselves, they don't produce text at all, they do all of this in conjunction with your inference engine. The model's output is a probability for every token. Constrained output is a feature of the inference engine. With a strict schema the inference engine can ignore every token that doesn't adhere to the schema and select the top token that does adhere to the schema.
        xigoi 2y ago
        Just because the answer adheres to the schema does not mean that it’s correct.
        furyofantares 2y ago
        Yes, we've been discussing "specific and exact" output. As I said, you might wish it called at different tool; nothing in this discussion is addressing that.
  - TeMPOraL 2y ago
    I'll need to work on the diagram to make it clearer next time. What it's trying to communicate is, in general, a human operating a computer has to turn their imprecise thinking into "specific and exact commands", and subsequently, understand the "specific and exact output" in whatever terms they're thinking off, prioritizing and filtering out data based on situational context. LLMs enter the picture in two places: 1) In many situations, they can do the "imprecise thinking" -> "specific and exact commands" step for the user; 2) In many situations, they can do the "specific and exact output" -> contextualized output step for the user; In such scenarios, LLMs are not replacing software, they're being slotted as intermediary between user and classical software, so the user can operate closer to what's natural for them, vs. translating between it and rigid computer language. This is not applicable everywhere, but then, this is also not the only way LLMs are useful - it's just one broad class of scenarios in which they are.
- namaria 2y ago
  Despite feeling like a "let me draw it for you" answer is a tad condescending, I want to address something here. This would be great if LLMs did not tend to output nonsense. Truly it would be grand. But they do. So it isn't. It's wasting resources hoping for a good outcome and risking frustration, misapprehensions, prompt injection attacks... It's non-deterministic algorithms hoping P=NP, except instead of branching at every decision you're doing search by tweaking vectors whose values you don't even know and whose influence on the outcome is impossible to foresee. Sure, a VC subsidized LLM is a great way to make CVs in LaTeX (I do it all the time), translating text, maybe even generating some code if you know what you need and can describe it well. I will give you that. I even created a few - very mediocre - songs. Am I contradicting myself? I don't think I am, because I would love to live in a hotel if I only had to pay a tiny fraction of the cost. But I would still think that building hotels would be a horrible way to address the housing crisis in modern metropolises.
  - TeMPOraL 2y ago
    > Despite feeling like a "let me draw it for you" answer is a tad condescending, I want to address something here. I didn't mean it to be condescending - though I can see how it can come across as such. FWIW, I opted for a diagram after I typed half a page worth of "normal" text and realized I'm still not able to elucidate my point - so I deleted it and drew something matching my message more closely. > This would be great if LLMs did not tend to output nonsense. Truly it would be grand. But they do. So it isn't. I find this critique to be tiring at this point - it's just as wrong as assuming LLMs work perfectly and all is fine. Both views are too definite, too binary. In reality, LLMs are just non-deterministic - that is, they have an error rate. How big it is, and how small can it get in practice for a given tasks - those are the important questions. Pretty much every aspect of computing is only probabilistically correct - either because the algorithm is explicitly so (UUIDs and primality testing, for starters), or just because it runs on real hardware, and physics happen. Most people get away with pretending that our systems are either correct or not, but that's only possible because the error rate is low enough. But it's never that low by accident - it got pushed there by careful design at every level, hardware and software. LLMs are just another probabilistically correct system that, over time, we'll learn how to use in ways that gets the error rate low enough to stop worrying about it. How can we get there - now, that is an interesting challenge.
brookst 2y ago
I also don’t like command like interfaces for all things, but there are cases where they excel, or where they are necessary due to technical constraints. But when the man page for a simple command runs to 10 screens of options I sometimes wonder.
grbsh 2y ago
Why would you ever hire a human to perform some task for you in a company? They're known for having problems with ambiguity and precision in communication. Humans require a lot of back and forth effort for "alignment" with regular "syncs" and "iterations" and "I'll get that to you by EOD". If you approach the potential of natural interfaces with expectations that frame them the same way as 2000s era software, you'll fail to be creative about new ways humans interact with these systems in the future.

Two key things that make computers useful, specificity and exactitude, are thrown out of the window by interposing NLP between the person and the computer. I don't get it at all.

TeMPOraL 2y ago

[imprecise thinking] v <--- LLMs do this for you [specific and exact commands] v [computers] v [specific and exact output] v <--- LLMs do this for you [contextualized output] In many cases, you don't want or need that. In some, you do. Use right tool for the job, etc.

shakna 2y ago
I don't think they give a specific and exact output, considering how nondeterminism plays a role in most models.
- furyofantares 2y ago
  The diagram you're replying to agrees with this.
  - walthamstow 2y ago
    Does it? The way I'm reading it, the first step is LLM turning human imprecise thinking into specific and exact commands
    - furyofantares 2y ago
      That's true, but that is the input section of the diagram, not the output section where [specific and exact output] is labeled, so I believe there was legitimate confusion I was responding to. To your point, which I think is separate but related, that IS a case where LLMs are good at producing specific and exact commands. The models + the right prompt are pretty reliable at tool calling by themselves, because you give them a list of specific and exact things they can do. And they can be fully specific and exact at inference time with constrained output (although you may still wish it called a different tool.)
      - shakna 2y ago
        The tool may not even exist. LLMs are really terrible at admitting where the limits of the training are. They will imagine a tool into being. They will also claim the knowledge is within their realm, when it isn't.
        furyofantares 2y ago
        At inference time you can constrain output to a strict json schema that only includes valid tools.
        shakna 2y ago
        That would only be possible, if you could prevent hallucinations from ever occurring. Which you can't. Even if you supply a strict schema, the model will sometimes act outside of it - and infer the existence of "something similar".
        furyofantares 2y ago
        That's not true. You say the model will sometimes act outside of the schema, but models don't act at all, they don't hallucinate by themselves, they don't produce text at all, they do all of this in conjunction with your inference engine. The model's output is a probability for every token. Constrained output is a feature of the inference engine. With a strict schema the inference engine can ignore every token that doesn't adhere to the schema and select the top token that does adhere to the schema.
        xigoi 2y ago
        Just because the answer adheres to the schema does not mean that it’s correct.
        furyofantares 2y ago
        Yes, we've been discussing "specific and exact" output. As I said, you might wish it called at different tool; nothing in this discussion is addressing that.
- TeMPOraL 2y ago
  I'll need to work on the diagram to make it clearer next time. What it's trying to communicate is, in general, a human operating a computer has to turn their imprecise thinking into "specific and exact commands", and subsequently, understand the "specific and exact output" in whatever terms they're thinking off, prioritizing and filtering out data based on situational context. LLMs enter the picture in two places: 1) In many situations, they can do the "imprecise thinking" -> "specific and exact commands" step for the user; 2) In many situations, they can do the "specific and exact output" -> contextualized output step for the user; In such scenarios, LLMs are not replacing software, they're being slotted as intermediary between user and classical software, so the user can operate closer to what's natural for them, vs. translating between it and rigid computer language. This is not applicable everywhere, but then, this is also not the only way LLMs are useful - it's just one broad class of scenarios in which they are.
namaria 2y ago
Despite feeling like a "let me draw it for you" answer is a tad condescending, I want to address something here. This would be great if LLMs did not tend to output nonsense. Truly it would be grand. But they do. So it isn't. It's wasting resources hoping for a good outcome and risking frustration, misapprehensions, prompt injection attacks... It's non-deterministic algorithms hoping P=NP, except instead of branching at every decision you're doing search by tweaking vectors whose values you don't even know and whose influence on the outcome is impossible to foresee. Sure, a VC subsidized LLM is a great way to make CVs in LaTeX (I do it all the time), translating text, maybe even generating some code if you know what you need and can describe it well. I will give you that. I even created a few - very mediocre - songs. Am I contradicting myself? I don't think I am, because I would love to live in a hotel if I only had to pay a tiny fraction of the cost. But I would still think that building hotels would be a horrible way to address the housing crisis in modern metropolises.
- TeMPOraL 2y ago
  > Despite feeling like a "let me draw it for you" answer is a tad condescending, I want to address something here. I didn't mean it to be condescending - though I can see how it can come across as such. FWIW, I opted for a diagram after I typed half a page worth of "normal" text and realized I'm still not able to elucidate my point - so I deleted it and drew something matching my message more closely. > This would be great if LLMs did not tend to output nonsense. Truly it would be grand. But they do. So it isn't. I find this critique to be tiring at this point - it's just as wrong as assuming LLMs work perfectly and all is fine. Both views are too definite, too binary. In reality, LLMs are just non-deterministic - that is, they have an error rate. How big it is, and how small can it get in practice for a given tasks - those are the important questions. Pretty much every aspect of computing is only probabilistically correct - either because the algorithm is explicitly so (UUIDs and primality testing, for starters), or just because it runs on real hardware, and physics happen. Most people get away with pretending that our systems are either correct or not, but that's only possible because the error rate is low enough. But it's never that low by accident - it got pushed there by careful design at every level, hardware and software. LLMs are just another probabilistically correct system that, over time, we'll learn how to use in ways that gets the error rate low enough to stop worrying about it. How can we get there - now, that is an interesting challenge.

brookst 2y ago

I also don’t like command like interfaces for all things, but there are cases where they excel, or where they are necessary due to technical constraints. But when the man page for a simple command runs to 10 screens of options I sometimes wonder.

grbsh 2y ago

Why would you ever hire a human to perform some task for you in a company? They're known for having problems with ambiguity and precision in communication. Humans require a lot of back and forth effort for "alignment" with regular "syncs" and "iterations" and "I'll get that to you by EOD". If you approach the potential of natural interfaces with expectations that frame them the same way as 2000s era software, you'll fail to be creative about new ways humans interact with these systems in the future.