4 ms·

I'm biased but I wouldn't call it misleading - generating text is super awesome and flexible, (we describe that in the blog post - and I personally use string m

I'm biased but I wouldn't call it misleading - generating text is super awesome and flexible, (we describe that in the blog post - and I personally use string models all the time) but it's true you pay a high tax for autoregressive generation > Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a completely wrong valid value. that is likely true of all ML! perhaps we could debate semantics, but I don't think it's fair to say a random forest "hallucinates" in the way LLMs do

zozbot234 19d ago
From a quick look at this it looks like it could easily generate natural language text by following a structured representation like UMR (Uniform Meaning Representation) or the similar representation the Abstract-Wikipedia folks will be working on for generic encyclopedic text (which will be heavily informed by Universal Dependencies). These are basically linguistically principled and frame-based counterparts to a programming language AST, that can be then converted to natural language (in a broadly language-independent way, to the extent that semantics and pragmatics make that feasible) via some sort of NLG rendering. (To be clear, this one raw model does not support outputing a full AST directly - it wants to output "choice" among fixed options, "score" on a sliding scale, or a true/false answer (all of these with confidence scores attached), so building the AST/structure would be a code-driven (or even perhaps outside LLM-driven in some more challenging cases) multi-step affair where the model would essentially be playing a "game" of building the structured output step by step and getting a revised partial state back. But one could expect this to lead to interesting results.)
- deleted 19d ago
  [deleted]
seizethecheese 19d ago
Just to be sure that I understand, you're saying that your model "can't hallucinate" because it only outputs a single thing, right? In this way, an LLM can't hallucinate either if I prompt it to do a classification task with a discrete set of possible outputs, right? (Assuming I reject non-conforming output. Actually, maybe what you're saying is that your system can't output non-conforming output?)
- zenlikethat 19d ago
  Yeah that's precisely correct. For e.g. classification tasks, even in 2026 people are doing things like hallucinating deliberately, and then matching the hallucinations to embeddings - https://softwaredoug.com/blog/2026/08/10/hypothetical-classi https://softwaredoug.com/blog/2026/08/10/hypothetical-classi... With TypeSafe it just picks the class (actually probabilities across classes), reliably every single time.
  - monkpit 19d ago
    404?
    - Galilyou 18d ago
      https://softwaredoug.com/blog/2026/08/10/hypothetical-classifications https://softwaredoug.com/blog/2026/08/10/hypothetical-classi...
    - deleted 19d ago
      [deleted]
dfee 19d ago
> I'm biased but I wouldn't call it misleading - @CompleteSkeptic Very strange.
WhitneyLand 19d ago
His claim was that the title is misleading, not sure how it's relevant to that claim that you use "string models" (full LLMs). The original title before it changed less than an hour ago was: "Jev: New frontier model 40-400x cheaper and 20-200x faster" I'm going to agree that was misleading. And on the second point: >>Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a completely wrong valid value. >that is likely true of all ML! perhaps we could debate semantics, but I don't think it's fair to say a random forest "hallucinates" in the way LLMs do" Also going to disagree here, and I don't think it's semantics. Type safety is not factual correctness.
- CompleteSkeptic 19d ago
  > Type safety is not factual correctness. I very much agree with this and want to hone in on where do actually disagree. Would you say a linear classifier hallucinates?
  - elcomet 19d ago
    Hallucinations were defined in the context of text generation models so your question does not really make sense. IMO your system can make mistakes that are similar in spirit to hallucination (i.e. answering with a false answer instead of abstaining to answer).
    - bigglebear 19d ago
      And furthermore, because the model is forced to answer in a boolean (if in boolean mode), if the user input is outside of the range of a boolean, it's forced to hallucinate. It can't abstain.
  - WhitneyLand 19d ago
    Let's say classifiers don't hallucinate. To make a fair comparison we should constrain LLMs to the same classification task. In that case, no, LLMs also don't hallucinate. - Give Jev and LLM the same input - Lock down both to approved/rejected/unknown (LLM restricts on decoding) - Both can be wrong, but neither can hallucinate (invent an another option).

zozbot234 19d ago

From a quick look at this it looks like it could easily generate natural language text by following a structured representation like UMR (Uniform Meaning Representation) or the similar representation the Abstract-Wikipedia folks will be working on for generic encyclopedic text (which will be heavily informed by Universal Dependencies). These are basically linguistically principled and frame-based counterparts to a programming language AST, that can be then converted to natural language (in a broadly language-independent way, to the extent that semantics and pragmatics make that feasible) via some sort of NLG rendering. (To be clear, this one raw model does not support outputing a full AST directly - it wants to output "choice" among fixed options, "score" on a sliding scale, or a true/false answer (all of these with confidence scores attached), so building the AST/structure would be a code-driven (or even perhaps outside LLM-driven in some more challenging cases) multi-step affair where the model would essentially be playing a "game" of building the structured output step by step and getting a revised partial state back. But one could expect this to lead to interesting results.)

deleted 19d ago
[deleted]

seizethecheese 19d ago

Just to be sure that I understand, you're saying that your model "can't hallucinate" because it only outputs a single thing, right? In this way, an LLM can't hallucinate either if I prompt it to do a classification task with a discrete set of possible outputs, right? (Assuming I reject non-conforming output. Actually, maybe what you're saying is that your system can't output non-conforming output?)

zenlikethat 19d ago
Yeah that's precisely correct. For e.g. classification tasks, even in 2026 people are doing things like hallucinating deliberately, and then matching the hallucinations to embeddings - https://softwaredoug.com/blog/2026/08/10/hypothetical-classi https://softwaredoug.com/blog/2026/08/10/hypothetical-classi... With TypeSafe it just picks the class (actually probabilities across classes), reliably every single time.
- monkpit 19d ago
  404?
  - Galilyou 18d ago
    https://softwaredoug.com/blog/2026/08/10/hypothetical-classifications https://softwaredoug.com/blog/2026/08/10/hypothetical-classi...
  - deleted 19d ago
    [deleted]

dfee 19d ago

> I'm biased but I wouldn't call it misleading - @CompleteSkeptic Very strange.

WhitneyLand 19d ago

His claim was that the title is misleading, not sure how it's relevant to that claim that you use "string models" (full LLMs). The original title before it changed less than an hour ago was: "Jev: New frontier model 40-400x cheaper and 20-200x faster" I'm going to agree that was misleading. And on the second point: >>Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a completely wrong valid value. >that is likely true of all ML! perhaps we could debate semantics, but I don't think it's fair to say a random forest "hallucinates" in the way LLMs do" Also going to disagree here, and I don't think it's semantics. Type safety is not factual correctness.

CompleteSkeptic 19d ago
> Type safety is not factual correctness. I very much agree with this and want to hone in on where do actually disagree. Would you say a linear classifier hallucinates?
- elcomet 19d ago
  Hallucinations were defined in the context of text generation models so your question does not really make sense. IMO your system can make mistakes that are similar in spirit to hallucination (i.e. answering with a false answer instead of abstaining to answer).
  - bigglebear 19d ago
    And furthermore, because the model is forced to answer in a boolean (if in boolean mode), if the user input is outside of the range of a boolean, it's forced to hallucinate. It can't abstain.
- WhitneyLand 19d ago
  Let's say classifiers don't hallucinate. To make a fair comparison we should constrain LLMs to the same classification task. In that case, no, LLMs also don't hallucinate. - Give Jev and LLM the same input - Lock down both to approved/rejected/unknown (LLM restricts on decoding) - Both can be wrong, but neither can hallucinate (invent an another option).