3 ms·

Hi, remilouf. You say that your background is in "probabilistic, relational and symbolic programming". In that case I suspect you understand that it is no probl

Hi, remilouf. You say that your background is in "probabilistic, relational and symbolic programming". In that case I suspect you understand that it is no problem to generate text from a regular or context-free grammar, or really any level of grammar. For example, you can do that very easily in Prolog (a relational language) given a grammar in Definite Clause Grammars notation. As far as I can tell your approach requires a grammar to be given by a user. In that case, what is the advantage of using an LLM to generate text? Why can't you just run your grammar as a generator and generate the text you want? That would save you the considerable trouble and cost of training an LLM in the first place. And why would you need an LLM, a model of natural language, if all you want is to generate structured text, anyway?

IanCal 3y ago
Wouldn't that generate an entirely random but valid output? Here you want a valid output related to the request. > And why would you need an LLM, a model of natural language, if all you want is to generate structured text, anyway? So that you can parse unstructured text from a person and return structured data for a machine.
- YeGoblynQueenne 3y ago
  >> Wouldn't that generate an entirely random but valid output? No. Grammars don't generate entirely random output. Even Probabilistic Context Free Grammars can generate deterministic output, depending on how they are sampled. The output can be related to some input, if desired, for example one can give a string with "holes" (variables) as input and have the holes filled-in by the grammar. >> So that you can parse unstructured text from a person and return structured data for a machine. If you are willing to spend the effort to write a grammar, you can do that without an LLM.
  - IanCal 3y ago
    I wasn't talking about deterministic Vs nondeterministic. > If you are willing to spend the effort to write a grammar, you can do that without an LLM. How are you taking, for example, a request to make a "fun but not over the top character from the middle ages, with relevant weapons and a backstory. Game theme is a world populated by anthropomorphic vegetables." And get back a character for the game in a specific JSON format without the LLM in your design here? That's not encodable in the grammar.
    - YeGoblynQueenne 3y ago
      As far as I can tell you won't be able to use the approach proposed here to create a character matching your above description unless every element of it is encoded in the guiding grammar (including the possibility for the character to have middle ages-relevant weapons, and the anthropomorphic vegetables). At which point, again I have to ask: what do you need the LLM for? You've already done all the hard work by hand and the LLM is only adding some extraneous natural language parsing on top. Plus, if you already have the grammar that can cover the anthropomorphic vegetable world it's only a bit more work to use it to parse such natural language requests, anyway. I think people forget that grammars were the staple for parsing natural language and stuffing it into structured form for a very long time before LLMs, and they still mostly are. The point is that if you have structure, someone has to hand-craft that structure. Frex, if you have a language with a compiler, someone has to write the compiler. Then, if you want to make some unstructured text conform to your hand-crafted structure, you can only do that to the extent that the unstructured text itself is made up of elements of the structured form. If you have a grammar for frogs and blueberries, and write a poem about the dawn and foxes, you can't use the former to structure the latter, no matter what you do, and LLMs won't make this happen magickally, either. Essentially, your grammar is a type and any unstructured text you want to convert to a structure with your grammar must be a value that you can cast to that type. >> I wasn't talking about deterministic Vs nondeterministic. Then what? What do you mean by "random string"?
      - creatonez 3y ago
        > I think people forget that grammars were the staple for parsing natural language and stuffing it into structured form for a very long time before LLMs, and they still mostly are. This is a rewritten history of natural language processing tech. Years of fine-tuned theory-heavy grammar coding for parsing and generating human language got the field basically nowhere.
      - IanCal 3y ago
        > As far as I can tell you won't be able to use the approach proposed here to create a character matching your above description unless every element of it is encoded in the guiding grammar (including the possibility for the character to have middle ages-relevant weapons, and the anthropomorphic vegetables). You wouldn't need to, that's the point here. You let the LLM work on generating semantically valid responses and use a tool like this to restrict it to syntactically correct ones. Here's an example jsonschema (a bit handwritten so maybe some errors but it should be clear enough). Let the LLM deal with coming up with a name and backstory that work, making sure the description and type of the weapon make sense (gpt4 suggested a close range carrot dagger for example), and let this work as your type structure. { "type": "object", "title": "character", "properties": { "backstory": { "type": "string" }, "weapons": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "description": { "type": "string" }, "weapon_type": { "type": "string", "enum": ["ranged", "close", "magic"] }, "range": { "minimum": 0, "maximum": 150 }, "damage": { "type": "number" } }, "required": [ "name", "description", "range", "damage" ] } }, "name": { "type": "string" } }, "required": [ "backstory", "weapons", "name" ] } > Then what? What do you mean by "random string"? Nonsense. Like "Colorless green ideas sleep furiously" the famous sentence that's grammatically correct but utter nonsense. > Plus, if you already have the grammar that can cover the anthropomorphic vegetable world it's only a bit more work to use it to parse such natural language requests, anyway. I really do not think this is the case. Parsing and understanding arbitrary requests about something like this?
Silasdev 3y ago
The idea is not to just generate any random string that matches the grammar. The idea is that if your request is "What are the first 10 digits of pi?" and you restrict the response to the regex: "[0-9]+\.[0-9]+", then you actually receive a correct answer of "3.1415926535" and not just a random string such as "1.2346789", which also happens to match the pattern.
- YeGoblynQueenne 3y ago
  That will only work up to the point when the LLM can't generate a correct answer, whether conforming to a grammar or not. After that point, you'll just get grammatically correct bullshit. Also, as noted in my reply to a sibling comment, grammars do not generate "any random string". That's the whole point of a grammar, that the generation is not random. For example it is perfectly feasible to write a grammar that completes a sentence with missing words, or continues some text etc. And to be clear, it is entirely feasible to write a grammar that takes some string as input and generates a string as output that is a transformation of the input string satisfying some constraint. This kind of grammar is known as a transducer. None of this should come as a surprise. Statistical language models are simply an alternative to knowledge-engineered grammars, used to do the same things that one can do with a grammar (except for the determinism). In a broad sense, a statistical language model is a kind of grammar, or perhaps it makes more sense to say that a grammar is a deterministic language model.
remilouf 3y ago
IanCal said it all. But for alternative approaches that also use LLM (with miniKanren) you can check https://arxiv.org/abs/1809.02840 https://arxiv.org/abs/1809.02840
- YeGoblynQueenne 3y ago
  See reply to IanCal's comment then. Later edit: you have a nice way to generate unstructured text, and now you want to go and bolt a structured representation on top. So now you have to do all the hard work by hand, again, to write the structured representation. That sounds like a regression. I'll have to make time to read your paper, thanks for linking it.

IanCal 3y ago

Wouldn't that generate an entirely random but valid output? Here you want a valid output related to the request. > And why would you need an LLM, a model of natural language, if all you want is to generate structured text, anyway? So that you can parse unstructured text from a person and return structured data for a machine.

YeGoblynQueenne 3y ago
>> Wouldn't that generate an entirely random but valid output? No. Grammars don't generate entirely random output. Even Probabilistic Context Free Grammars can generate deterministic output, depending on how they are sampled. The output can be related to some input, if desired, for example one can give a string with "holes" (variables) as input and have the holes filled-in by the grammar. >> So that you can parse unstructured text from a person and return structured data for a machine. If you are willing to spend the effort to write a grammar, you can do that without an LLM.
- IanCal 3y ago
  I wasn't talking about deterministic Vs nondeterministic. > If you are willing to spend the effort to write a grammar, you can do that without an LLM. How are you taking, for example, a request to make a "fun but not over the top character from the middle ages, with relevant weapons and a backstory. Game theme is a world populated by anthropomorphic vegetables." And get back a character for the game in a specific JSON format without the LLM in your design here? That's not encodable in the grammar.
  - YeGoblynQueenne 3y ago
    As far as I can tell you won't be able to use the approach proposed here to create a character matching your above description unless every element of it is encoded in the guiding grammar (including the possibility for the character to have middle ages-relevant weapons, and the anthropomorphic vegetables). At which point, again I have to ask: what do you need the LLM for? You've already done all the hard work by hand and the LLM is only adding some extraneous natural language parsing on top. Plus, if you already have the grammar that can cover the anthropomorphic vegetable world it's only a bit more work to use it to parse such natural language requests, anyway. I think people forget that grammars were the staple for parsing natural language and stuffing it into structured form for a very long time before LLMs, and they still mostly are. The point is that if you have structure, someone has to hand-craft that structure. Frex, if you have a language with a compiler, someone has to write the compiler. Then, if you want to make some unstructured text conform to your hand-crafted structure, you can only do that to the extent that the unstructured text itself is made up of elements of the structured form. If you have a grammar for frogs and blueberries, and write a poem about the dawn and foxes, you can't use the former to structure the latter, no matter what you do, and LLMs won't make this happen magickally, either. Essentially, your grammar is a type and any unstructured text you want to convert to a structure with your grammar must be a value that you can cast to that type. >> I wasn't talking about deterministic Vs nondeterministic. Then what? What do you mean by "random string"?
    - creatonez 3y ago
      > I think people forget that grammars were the staple for parsing natural language and stuffing it into structured form for a very long time before LLMs, and they still mostly are. This is a rewritten history of natural language processing tech. Years of fine-tuned theory-heavy grammar coding for parsing and generating human language got the field basically nowhere.
    - IanCal 3y ago
      > As far as I can tell you won't be able to use the approach proposed here to create a character matching your above description unless every element of it is encoded in the guiding grammar (including the possibility for the character to have middle ages-relevant weapons, and the anthropomorphic vegetables). You wouldn't need to, that's the point here. You let the LLM work on generating semantically valid responses and use a tool like this to restrict it to syntactically correct ones. Here's an example jsonschema (a bit handwritten so maybe some errors but it should be clear enough). Let the LLM deal with coming up with a name and backstory that work, making sure the description and type of the weapon make sense (gpt4 suggested a close range carrot dagger for example), and let this work as your type structure. { "type": "object", "title": "character", "properties": { "backstory": { "type": "string" }, "weapons": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "description": { "type": "string" }, "weapon_type": { "type": "string", "enum": ["ranged", "close", "magic"] }, "range": { "minimum": 0, "maximum": 150 }, "damage": { "type": "number" } }, "required": [ "name", "description", "range", "damage" ] } }, "name": { "type": "string" } }, "required": [ "backstory", "weapons", "name" ] } > Then what? What do you mean by "random string"? Nonsense. Like "Colorless green ideas sleep furiously" the famous sentence that's grammatically correct but utter nonsense. > Plus, if you already have the grammar that can cover the anthropomorphic vegetable world it's only a bit more work to use it to parse such natural language requests, anyway. I really do not think this is the case. Parsing and understanding arbitrary requests about something like this?

Silasdev 3y ago

The idea is not to just generate any random string that matches the grammar. The idea is that if your request is "What are the first 10 digits of pi?" and you restrict the response to the regex: "[0-9]+\.[0-9]+", then you actually receive a correct answer of "3.1415926535" and not just a random string such as "1.2346789", which also happens to match the pattern.

YeGoblynQueenne 3y ago
That will only work up to the point when the LLM can't generate a correct answer, whether conforming to a grammar or not. After that point, you'll just get grammatically correct bullshit. Also, as noted in my reply to a sibling comment, grammars do not generate "any random string". That's the whole point of a grammar, that the generation is not random. For example it is perfectly feasible to write a grammar that completes a sentence with missing words, or continues some text etc. And to be clear, it is entirely feasible to write a grammar that takes some string as input and generates a string as output that is a transformation of the input string satisfying some constraint. This kind of grammar is known as a transducer. None of this should come as a surprise. Statistical language models are simply an alternative to knowledge-engineered grammars, used to do the same things that one can do with a grammar (except for the determinism). In a broad sense, a statistical language model is a kind of grammar, or perhaps it makes more sense to say that a grammar is a deterministic language model.

remilouf 3y ago

IanCal said it all. But for alternative approaches that also use LLM (with miniKanren) you can check https://arxiv.org/abs/1809.02840 https://arxiv.org/abs/1809.02840

YeGoblynQueenne 3y ago
See reply to IanCal's comment then. Later edit: you have a nice way to generate unstructured text, and now you want to go and bolt a structured representation on top. So now you have to do all the hard work by hand, again, to write the structured representation. That sounds like a regression. I'll have to make time to read your paper, thanks for linking it.