4 ms·

I agree with most of your comment, but... > To name it "hallucination" is an euphemism... those are errors I find this and other "don't anthropomorphize the c

• john_strinlai 8d ago
  >language has always contained overloaded or "literally inaccurate" terms. "literally" is a great example of this, because it can also mean "not literally, but with emphasis".
• rrr_oh_man 8d ago
  Every output an LLM creates is a hallucination.
• bix6 8d ago
  Knowingly causing errors is not forgivable whereas hallucinations sounds esoteric and moves blame away from the people who are knowingly causing errors. It’s marketing speak.
• piker 8d ago
  I also agree with the parent, and I would also suggest "hallucination" is better than "error" which might imply an available deterministic correction. Hallucination makes it clear we're dealing with something different than an "error" or "bug".
  • orwin 7d ago
    I disagree, for me "error" is way, way more accurate than "hallucination", but i did take applied statistics in college and that might have influenced my vocabulary. Maybe that for the general public, "hallucination" is a better description, i might have biases in this case. But "error" is _definitely_ more accurate. If people want to call "drisse", "aussière", "balancine" and "ecoute" all as "boat ropes", they are correct. In english, i would certainly call them all "boat ropes" in any case, as i never needed to translate their names. It isn't the most accurate in my opinion, but as long as you're not working on them (or manning a boat in my analogy), who cares.
  • narnarpapadaddy 7d ago
    For humans hallucinations are a particular class of error, so I find hallucination more descriptive than either error or bug. I also think it’s relevant because a hallucinator often doesn’t recognize that the hallucination isn’t real. That’s more accurate for the LLM than either lie or confabulation, IMO. They algorithm is trained to produce strings of text that have semantic meaning based on some statistical likelihood of tokens appearing next to each other. The LLM algorithm is working as intended. Hallucinations are also often emergent from a particular state or situation, which reflects the generative aspect of LLMs. Hallucinations are sometimes resolved in humans by grounding exercises. “Touching grass.” The same is true for LLM hallucinations. Inaccuracies are found by cross-checking the output against an internet search or another LLM.
• Slow_Hand 8d ago
  I prefer “confabulation”. It seems truer to what is happening: The LLM isn’t seeing something that’s not there, but deliberately making up _something_ so that it can return a response.
• deleted 8d ago
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• usernomdeguerre 8d ago
  I disagree, I think 'Hallucination' is a risk-shedding weasel-word. It's meant to shift blame away from the technology and its creator (multibillion dollar AI companies etc) in a way that doesn't hold those actors accountable or responsible for the outcomes. In any other software it would be an error, regression, bug. And in a human process it would be at ~least something someone would call 'bullshit'.
  • vorticalbox 8d ago
    I’m not sure either would is particularly good at describing what is happening. Error in implies something broke, which nothing broke the LLM did exactly what they where designed to do generate text based on a statistically likely bases. Hallucination Does really fit here either. It implies it’s experiencing something that is not there which it isn’t experiencing anything.
    • Towaway69 8d ago
      Howabout: lied. The LLM lied indirectly (perhaps) but it made a claim that was false. Which is a lie. Humans lie and LLMs “hallucinate”? What gives. It’s an untruth that the LLM is selling for a truth, that’s lying in my books. And since we don’t know how or why the LLM works, we can’t even judge whether it explicitly lied or only because it didn’t know better.
      • vorticalbox 7d ago
        Lie implies it knows what it is saying to be false.
    • t-3 8d ago
      Unexpected Result is perhaps a more accurate description.
  • segsegsgsg 8d ago
    error, regression, bug, bullshit are not weasel words, hallucination is a weasel word because why exactly? your argument is a weasel argument.
• Rebuff5007 8d ago
  Note that "bug" came from an actual moth in a computer: https://www.computerhistory.org/tdih/september/9/ https://www.computerhistory.org/tdih/september/9/
  • john_strinlai 8d ago
    neat part of history, but i dont think that's what that says. the last sentence starts with "Originating with Thomas Edison in the 1800s, the term “bug” is still used [...]", and there would be no reason to use the word "actual" in the sentence "First _actual_ case of bug being found" if it was the origin of the term. my clanker found this: https://spectrum.ieee.org/did-you-know-edison-coined-the-term-bug https://spectrum.ieee.org/did-you-know-edison-coined-the-ter... "The use of “bug” to describe a flaw in the design or operation of a technical system dates back to Thomas Edison. He coined the phrase 140 years ago to describe technical problems during the process of innovation." the moth seems to be a popular misconception, though, given that the article starts with "Ask someone to identify the first computer bug, and he or she might mention computer programmer Grace Hopper and the dead moth found in a relay of Harvard University’s Mark II electromechanical computer in 1947"
  • Sharlin 8d ago
    I believe the word was already in use to denote a malfunction of any sort of machine or device. As such this was a bug (insect) that caused a bug (glitch); it was punny already in 1947.
• deleted 8d ago
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• cmiles74 8d ago
  Anthropomorphizing the tool led directly to this problem, where we nearly started a war with China.
• antonvs 8d ago
  The term “hallucination” is a projection of inappropriate expectations onto a program. We know that LLMs are not “truth machines,” but we really want them to be. So when they produce a result that happens not to match external reality - which, it should be noted, LLMs don’t generally have access to - we call it an hallucination. “Bugs” are completely different. With bugs, we have a clear specification and we have a program that’s supposed to meet that specification. If it doesn’t, we say the program has bugs, and if it’s important enough we can change the program to eliminate the bugs. You can try to apply similar logic to LLMs, but you’d be making a category error, and you’ll fail to get the results you want in general. It’s not the same thing at all. If anything, the concept of an LLM hallucination is a bug in human understanding of LLMs.
  • jyounker 8d ago
    The word "confabulation" is much more precise and appropriate than "hallucination". We should use it instead.
  • orwin 7d ago
    Yes, but when a statistical model give you an erroneous result, you call the output an error, not a bug. I think error is more appropriate here. The error can be a sampling error, an inference error, or yes, a software error (or bug)
• jyounker 8d ago
  From the point of view of the system, this is an error. It is incorrect information. The term "hallucination" feels much more like anthropomorphizing. The word hallucination implies an aberrant condition. A much better term would be "confabulation". You don't trust things or individuals that confabulate.
  • ChrisLTD 8d ago
    a filling in of gaps in memory through the creation of false memories by an individual who is affected with a memory disorder (as Korsakoff syndrome) and is unaware that the fabricated memories are inaccurate and false vs. a sensory perception (such as a visual image or a sound) that occurs in the absence of an actual external stimulus and usually arises from neurological disturbance (such as that associated with delirium tremens, schizophrenia, Parkinson's disease, or narcolepsy) or in response to drugs (such as LSD or phencyclidine)
    • pocksuppet 8d ago
      So call them confabulations
    • gizajob 8d ago
      Confabulation is also a symptom very prevalent in forms of narcissism and psychopathy. Gaps in understanding or perception are back-filled by confabulating so as to not risk the omnipotence of the confabulator. Up to the reader to decide whether this phenomenon is found in the statements of AI leadership or not.
      • t-3 8d ago
        It's also something people tend to do when thinking, daydreaming, trying to solve problems, etc. We just usually don't fall for our own bullshit. LLMs don't either. They just give output in response to input. If the output is wrong that's because the model is wrong, not because the LLM is doing anything it's not supposed to be. It just wasn't built well enough to produce the expected result.
  • 8d ago
• order-matters 8d ago
  hallucination is common language for these models at this point which describes a particular type of error where the models make shit up. it is noticeable that the form of this particular error holds a similar shape to what is casually described as hallucinations, in that there is a generated content that often appears to blend naturally into the rest of the output but is false. the term hallucination often invokes a caution that this particular type of error may be influential and believable and is particularly dangerous
• nonethewiser 8d ago
  Sure… but being wrong doesnt necessarily make it a hallucination: >It was only just before the planned operation that officials dug deeper into the report put together by a special operations command analyst and found it had been generated with the help of artificial intelligence (AI) — and that a chatbot the analyst had used inaccurately identified the material the ship was carrying. CNN was not able to learn what the misidentified cargo was.
• 0x20cowboy 8d ago
  It’s not an error or a hallucinations it works correctly every time, and statistically picks the next token for the sequence. Retuning inf or crashing would be an error. If you want to ascribe some kind of meaning to the tokens, then maybe the training data was insufficient to predict the token in the sequence you wanted, but it doesn’t predict the next “fact”, and it doesn’t “think” it predicts the next token.
  • margalabargala 8d ago
    LLMs are useful because (and inasmuch as) their output generally reflects coherent reality. And their output does, usually, reflect coherent reality. The problem class of "properly operating program emits output incompatible with coherent reality" is something that is reasonable to put under its own term, considering it's a new class of problem. In other words, I think you misunderstand the language others are using. "Hallucination" doesn't refer to an "error" in the sense that crashing is an error, it refers to a situation in the problem class above, which is compatible with it working correctly every time. > it doesn’t “think” it predicts the next token. I never said it did. And I agree that LLMs don't "think". That said I am fully willing to go to bat arguing "thinking tokens" is a perfectly fine piece of jargon. Metaphors are completely acceptable parts of language, and contextual meaning is something grasped by everyone including the pedants who pretend not to.
    • 0x20cowboy 7d ago
      > I think you misunderstand the language others are using. "Hallucination" doesn't refer to an "error" in the sense that crashing is an error, it refers to a situation in the problem class above, which is compatible with it working correctly every time. I do not misunderstand, I think maybe you do. You think there is a proper next word selection based on logic or meaning and there for the model selected the wrong one - it hallucinated. I am saying the model has no concept if anything other than the probability of select a token which is not based in any logic so it is working properly- it only works on numbers. It is random chance that it is ever correct, not that it is correct often and messed up this one time.
• jacquesm 8d ago
  OpenAI calls them 'mistakes'. But that's just a fig leaf. Google does it too: "AI responses may include mistakes." Mistakes have an air of innocence. But these are not mistakes, they are purposefully releasing stuff that they know is broken, they just don't know when it is broken...
  • s1artibartfast 7d ago
    Broken is a little hyperbolic. Lots of totally viable essential or everyday products are not perfectly reliable. Medicine is not 100% reliable. My car isn't 100% reliable. Hell, my phone and cellular network are not 100% reliable. They are all still extremely useful tools. I might want them to be even better, but that's a cost versus quality question.
• DanHulton 7d ago
  I’ll even go one step further - I don’t even like saying “Artificial Intelligence”. I think even that anthropomorphizes the machine too much. I prefer “Simulated Intelligence”, and I feel like that describes what is going on much better. We are, through this process, simulating intelligence. These models aren’t intelligent, but they can simulate it. Every simulation has a degree of fidelity, and we’re not at 100%, not even with the top models. When you think about it in those terms, I find it becomes a lot easier to keep their limitations in mind. Additionally, it becomes easier to remember that this is an algorithm that you are running, and are responsible for, not another being that you can ascribe blame to.
• elzbardico 6d ago
  They are neither hallucinations or errors. They are just generations that happen to not be grounded in facts from the real world.
  • margalabargala 6d ago
    > They are just generations that happen to not be grounded in facts from the real world. Right, yes, and "hallucination" is the term that a critical mass of people have chosen to use.as a shorthand so that we don't have to write out "generations that happen to not be grounded in facts from the real world" every time it happens.