| ▲ | CompleteSkeptic 5 hours ago |
| 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 |
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| ▲ | WhitneyLand 5 hours ago | parent | next [-] |
| 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. |
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| ▲ | CompleteSkeptic 5 hours ago | parent [-] | | > 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? | | |
| ▲ | InsideOutSanta an hour ago | parent | next [-] | | A hallucination in the context of LLMs is generally understood as an incorrect answer presented as factual. If you claim that "x can't hallucinate" in the context of LLMs, you're saying that x always gives accurate answers. It does not matter whether the answer is type safe. If its value is incorrect, it's a hallucination. | |
| ▲ | thduabmd 2 hours ago | parent | prev | next [-] | | No. Your launch post puts “0%” on a hallucination chart, then explains that the number comes from guaranteed schema matching. You’ve already agreed that this doesn’t establish correctness. An approve for an unauthorized action still meets the schema guarantee. That’s why I find the messaging misleading. You’re acknowledging the limitations in these replies while defending the broader reliability pitch. Even granting that each answer is calibrated individually, that doesn’t establish calibration of the decision that combines them. Sure, I can threshold a composite score, but there may be many wrong answers with the same score. An unauthorized action doesn’t become acceptable because it scores highly on the other dimensions. I still have to define the constraints and test which wrong actions get through the complete workflow on my own data. That’s a substantial part of the work being pushed back onto the developer. | |
| ▲ | bigglebear an hour ago | parent | prev | next [-] | | User input: "Hey, have your human support agent call me, tomorrow at 5pm." Model input: "Does the user want to speak to a human support agent?" Output: Yes. I imagine that your model would produce this, and I think it's fair to say this is a hallucination. A human would caveat it with: "Yes, but not right now.", your model is incapable of that. Yes is technically correct, but within the context of being in a live chat, a human would understand that the caveat is required. | |
| ▲ | elcomet 5 hours ago | parent | prev | next [-] | | 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 an hour ago | parent [-] | | 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. |
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| ▲ | 8note 2 hours ago | parent | prev | next [-] | | id say yes. a linear classifier that classifies between red and yellow balls will hallucinate on blue. linear regressions hallucinate in the simpson's paradox. the model output can be quite confident and not representative of reality | |
| ▲ | WhitneyLand 5 hours ago | parent | prev [-] | | 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). | | |
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| ▲ | seizethecheese 5 hours ago | parent | prev | next [-] |
| 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?) |
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| ▲ | zozbot234 5 hours ago | parent | prev | next [-] |
| 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.) |
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| ▲ | dfee 5 hours ago | parent | prev [-] |
| > I'm biased but I wouldn't call it misleading - @CompleteSkeptic Very strange. |
