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A guidance language for controlling LLMs

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Re: A guidance language for controlling LLMs

#141

Post by nomel »

startupsfail wrote: post #111
nomel wrote: post #93

Earlier quoted context omitted.

“You” is an optimization for the human user. Here’s some insight: https://news.ycombinator.com/item?id=35925154

If you see any prompt that starts with You, generally it is a poor design. Like using a “goto” or global variables.

This is true for something like raw GPT. For the chat models that have been specifically optimized for "you" prompts, this is false. See the discussion in the link I provided, along with the leaked copilot/bing prompts.

Or, in other words, use a model in a way that fully takes advantage of how it was specifically optimized, from the intentional burning of massive amounts of compute time/money to get it that way.

Re: A guidance language for controlling LLMs

#142

Post by jadefox »

DonaldPShimoda wrote: post #119
conradev wrote: post #107

Earlier quoted context omitted.

I don’t think formal languages are going anywhere because we need the guarantees that they can provide. From Dijkstra: https://www.cs.utexas.edu/users/EWD/transcriptions/EWD06xx/E... You need to be able to define all of the possible edge cases so there isn’t any Undefined Behavior: that’s the formal part Humans can use LLMs to manipulate these languages to achieve specific goals. I can imagine designing formal langua…

> LLMs, like humans, can manipulate these languages Absolutely not. LLMs do not "manipulate" language. They do not have agency. They are extremely advanced text prediction engines. Their output is the result of applying the statistics harvested and distilled from existing uses of natural language. They only "appear" human because they are statistically geared toward producing human-like sequences of words. They canno…

That “appearance” is pretty good at triggering our anthropomorphizing behaviors. I like your handle, did you read Richard Bach’s Illusions by any chance?

Re: A guidance language for controlling LLMs

#143

Post by [deleted] »

DonaldPShimoda wrote: post #119
conradev wrote: post #107

Earlier quoted context omitted.

I don’t think formal languages are going anywhere because we need the guarantees that they can provide. From Dijkstra: https://www.cs.utexas.edu/users/EWD/transcriptions/EWD06xx/E... You need to be able to define all of the possible edge cases so there isn’t any Undefined Behavior: that’s the formal part Humans can use LLMs to manipulate these languages to achieve specific goals. I can imagine designing formal langua…

> LLMs, like humans, can manipulate these languages Absolutely not. LLMs do not "manipulate" language. They do not have agency. They are extremely advanced text prediction engines. Their output is the result of applying the statistics harvested and distilled from existing uses of natural language. They only "appear" human because they are statistically geared toward producing human-like sequences of words. They canno…

[deleted]

Re: A guidance language for controlling LLMs

#144

Post by m3kw9 »

evanmays wrote: post #67
m3kw9 wrote: post #50

I’m not understanding how Guidence Accelerating works. It says “ This cuts this prompt's runtime in half vs. a standard generation approach.” and it gives an example of it asking LLM to generate json. I don’t see anywhere how it accelerates anything because it’s a simple json completion call. How can you accelerate that?

The interface makes it look simple, but under the hood it follows a similar approach to jsonformer/clownfish [1] passing control of generation back and forth between a slow LLM and relatively fast python Let's say you're halfway through a generation of a json blob with a name field and a job field and have already generated { "name": "bob" At this point, guidance will take over generation control from the model to ge…

Thanks for the cool response. Would this use a lot more input token if I’m understanding this correctly because you are stopping the generation after a single fill and then generating again and inputing that for another token?

Re: A guidance language for controlling LLMs

#146

Post by chaxor »

ubj wrote: post #38

I like this step towards greater rigor when working with LLM's. But part of me can't help but feel like this is essentially reinventing the concept of programming languages: formal and precise syntax to perform specific tasks with guarantees. I wonder where the final balance will end up between the ease and flexibility of everyday language, and the precision / guarantees of a formally specified language.

It's rigor applied where we don't need it, and ignores where we do (mathematical proofs and NN theory, architecture, hyper parameters, training schemes, etc).

I have a somewhat irrational hatred towards almost all of the prompt oriented stuff being thrown about recently. There are a few (very few) input related training schemes that are interesting, but quite a bit of the "proompt-physicians" are just heralding the idea of essentially 'concise and effective communication' as 'I'm a ML expert now' ... which is annoying.

Re: A guidance language for controlling LLMs

#147

Post by woah »

nomel wrote: post #93
startupsfail wrote: post #89

Earlier quoted context omitted.

We really need to start thinking of how to reduce magical thinking in the field. It’s not pretty. They literally quote biblical guidance for the models and pray that this would work. And start their prompts with “You”. Who is “You”?

“You” is an optimization for the human user. Here’s some insight: https://news.ycombinator.com/item?id=35925154

Functions are an optimization for the human user

Re: A guidance language for controlling LLMs

#148

Post by Der_Einzige »

chaxor wrote: post #146
ubj wrote: post #38

I like this step towards greater rigor when working with LLM's. But part of me can't help but feel like this is essentially reinventing the concept of programming languages: formal and precise syntax to perform specific tasks with guarantees. I wonder where the final balance will end up between the ease and flexibility of everyday language, and the precision / guarantees of a formally specified language.

It's rigor applied where we don't need it, and ignores where we do (mathematical proofs and NN theory, architecture, hyper parameters, training schemes, etc). I have a somewhat irrational hatred towards almost all of the prompt oriented stuff being thrown about recently. There are a few (very few) input related training schemes that are interesting, but quite a bit of the "proompt-physicians" are just heralding the i…

Why would you dislike actual prompt engineering? This isn't some grifter trying to claim they're an expert because they wrote a cool prompt, this is a full fledged structured templating system for LLMs from an excellent author whose done a ton of other ML work.

I think you should attack actual grifters instead of an excellent project.

Re: A guidance language for controlling LLMs

#149

Post by chefandy »

ubj wrote: post #38

I like this step towards greater rigor when working with LLM's. But part of me can't help but feel like this is essentially reinventing the concept of programming languages: formal and precise syntax to perform specific tasks with guarantees. I wonder where the final balance will end up between the ease and flexibility of everyday language, and the precision / guarantees of a formally specified language.

Maybe someone will make an LLM with equivalent functionality to python that you can conveniently control with python syntax.

Re: A guidance language for controlling LLMs

#150

Post by brookst »

cubefox wrote: post #139
jameshart wrote: post #128

Earlier quoted context omitted.

The result is actually richer than ‘predicted output’ - it’s a probability distribution over all possible output. Having richer ways to consume that probability distribution than just ‘take the most likely thing, after adding some noise’ is more conducive to using LLMs to generate output that can be further processed - in rigorous ways. Like by running it through a compiler. Think about how when you’re coding, autoco…

Note that for any fine-tuned models (like GPT-4, where the foundation model has not been made accessible) the model does no longer give the "probabilities" of the next tokens, but rather their "goodness". Where the numbers say how good a token would be relative to the aims the model inferred from its fine-tuning.

Isn’t that the same thing? The non-fine-tuned models also have assumptions based on corpus and training. I don’t think there’s such a thing as a purely objective probability of the next token.
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