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> We can be pretty sure they are not hallucinations. Everything from LLMs are hallucinations. They don’t store facts. They store language patterns. Their outp

> We can be pretty sure they are not hallucinations. Everything from LLMs are hallucinations. They don’t store facts. They store language patterns. Their output semantically matching reality is not something that can ever be counted on. LLMs don’t deal with semantics at all. All semantics are provided by the user.

d-z-m 3y ago
> Everything from LLMs are hallucinations. People use the term "hallucination" to refer to output from LLMs that is factually incorrect. So if the LLM says "Water is two parts hydrogen and one part oxygen" that is not a hallucination.
- diputsmonro 3y ago
  It is still a hallucination even if the words it hallucinates happen to line up with a factual sentence, in the same way that a broken clock happens to correctly display the time twice a day. The function of the clock does not suddenly begin working correctly for one minute and then stop working correctly the next. The function of a broken clock is always flawed. Those broken outputs, by pure coincidence, just happen to be correct sometimes. LLMs are broken in the same way. They are just predictive text generators, with no real knowledge of concepts or reasoning. As it happens most of the text it has been trained on is factual, so when it regurgitates that text it is only by happenstance, not function, that it produces facts. When it hallucinates a completely new sentence by mashing its learned texts together, it's pure chance whether the resulting sentence is truthful or not. Every generation is a hallucination. Some hallucinations happen to be sentences that reflect the truth. The LLM has no ability to tell the difference.
  - d-z-m 3y ago
    You're using a different definition of "hallucination" than the one most people use when talking about LLMs. If you want to do that that's fine, but you're definitely in the minority.
    - SrslyJosh 3y ago
      Most people anthropomorphize LLMs. That doesn't make them right. It's a bad term, and one that misunderstands what LLMs are doing. An LLM is doing the exact same thing when it generates output that you consider to be a "hallucination" that it's doing when it generates output that you consider "correct".
      - simonw 3y ago
        What's your alternative suggestion for a term we can use to describe instances where an LLM produces a statement that appears to be factual (the title and authors of a paper for example) but is in fact entirely made up and doesn't reflect the real world at all?
        dleeftink 3y ago
        Similar to cache 'hits or misses', I always thought the idea of the underlying 'knowledge cache' being exhausted (i.e. its embedding space) would fit the bill nicely. Another way of framing it would be along the lines of 'catastrophic backtracking' but attenuated: a transformer attention head veering off the beaten path due to query/parameter mismatches. These are by no means exhaustive or complete, but I would suggest knowledge exhaustion, stochastic backtracking, wayward branching or simply perplexion. Verbiage along the lines of misconstrue, fabricate and confabulate have anecdotally been used to describe this state of perplexity.
        diputsmonro 3y ago
        A coincidence. Like, it's a bit sarcastic, sure, but until factuality is explicitly built into the model, I don't think we should use any terminology that implies that the outputs are trustworthy in any way. Until then, every output is like a lucky guess. Similar to a student flipping a coin to answer a multiple choice test. Though they get the correct answer sometimes, it says nothing at all about what they know, or how much we can trust them when we ask a question that we don't already know the answer to. Every LLM user should keep that in mind.
        chriskanan 3y ago
  - insanitybit 3y ago
    That's like saying that I'm hallucinating right now by reading your post and interpreting the words, it just happens to be that I'm reading your post as it is written. Most people call that "thinking".
  - justanotherjoe 3y ago
    Just because it inputs and outputs text embeddings, doesnt mean its all text in between. Inside, it doesnt work in units of texts. You wouldnt say a blind human is just a text pattern machine cause it inputs and outputs text. Theres nothing stopping the llm to learn a rich semantic model of the real world in its 100s of billions of params
- SrslyJosh 3y ago
  What is occurring inside the LLM that differs in these two cases? I don't think that you can demonstrate a difference. The term obscures more than it illuminates.
  - tentacleuno 3y ago
    What's different is that for a hallucination, the AI gets it wrong, and for a non-hallucination, the AI got it right.
IshKebab 3y ago
Ah you read [this nonsense](https://news.ycombinator.com/item?id=37874174 https://news.ycombinator.com/item?id=37874174).
Sharlin 3y ago
Do you store "facts"? How can you be sure? Want to prove it for me? Would you like to define "fact" and "language pattern" to me such that the definitions are mutually exclusive?
- krainboltgreene 3y ago
  This is the most common type of response to any realistic look at LLMs, it's always hilarious. Who are you convincing by using another field of research you also don't understand?
  - wongarsu 3y ago
    Of course LLMs don't store facts. They only "experience" the world through text tokens, so at best they can store and process information about text tokens, and any information that can be inferred from those. But that's exactly what philosophy has been arguing about regarding humans since at least Descartes's Evil Demon (the 17th century version of the brain in a vat). Humans don't know anything about "reality", they only know what their senses are telling them. Which is at best a very skewed and limited view of reality, and at worst completely wrong or an illusion. We perceive the world through more facets than an LLM, but fundamentally we share many of their limitations. So if someone says "LLMs don’t store facts", I find "neither do humans" a very reasonable answer, even if its only purpose is to show that "can it store facts" is a bad metric. Of course the more productive part to argue about is the "are facts and language patterns really mutually exclusive", which leads right into "if you had to design an efficient token predictor, would it do 'dumb' math like a markov chain, or would your design include some kind of store of world knowledge? Can you encode a knowledge store in a neural network? And if you can, how can you tell that LLMs don't do that internally?"
    - krainboltgreene 3y ago
      I get that high school philosophy discussions are fun, but it exceptionally weird when it seems to only come up when people doubt the intelligence of a LLM.
spdustin 3y ago
Yes, and in this case, the positional encoding of the tokens used in the system message favored returning them verbatim when asked to return them verbatim.

d-z-m 3y ago

> Everything from LLMs are hallucinations. People use the term "hallucination" to refer to output from LLMs that is factually incorrect. So if the LLM says "Water is two parts hydrogen and one part oxygen" that is not a hallucination.

diputsmonro 3y ago
It is still a hallucination even if the words it hallucinates happen to line up with a factual sentence, in the same way that a broken clock happens to correctly display the time twice a day. The function of the clock does not suddenly begin working correctly for one minute and then stop working correctly the next. The function of a broken clock is always flawed. Those broken outputs, by pure coincidence, just happen to be correct sometimes. LLMs are broken in the same way. They are just predictive text generators, with no real knowledge of concepts or reasoning. As it happens most of the text it has been trained on is factual, so when it regurgitates that text it is only by happenstance, not function, that it produces facts. When it hallucinates a completely new sentence by mashing its learned texts together, it's pure chance whether the resulting sentence is truthful or not. Every generation is a hallucination. Some hallucinations happen to be sentences that reflect the truth. The LLM has no ability to tell the difference.
- d-z-m 3y ago
  You're using a different definition of "hallucination" than the one most people use when talking about LLMs. If you want to do that that's fine, but you're definitely in the minority.
  - SrslyJosh 3y ago
    Most people anthropomorphize LLMs. That doesn't make them right. It's a bad term, and one that misunderstands what LLMs are doing. An LLM is doing the exact same thing when it generates output that you consider to be a "hallucination" that it's doing when it generates output that you consider "correct".
    - simonw 3y ago
      What's your alternative suggestion for a term we can use to describe instances where an LLM produces a statement that appears to be factual (the title and authors of a paper for example) but is in fact entirely made up and doesn't reflect the real world at all?
      - dleeftink 3y ago
        Similar to cache 'hits or misses', I always thought the idea of the underlying 'knowledge cache' being exhausted (i.e. its embedding space) would fit the bill nicely. Another way of framing it would be along the lines of 'catastrophic backtracking' but attenuated: a transformer attention head veering off the beaten path due to query/parameter mismatches. These are by no means exhaustive or complete, but I would suggest knowledge exhaustion, stochastic backtracking, wayward branching or simply perplexion. Verbiage along the lines of misconstrue, fabricate and confabulate have anecdotally been used to describe this state of perplexity.
      - diputsmonro 3y ago
        A coincidence. Like, it's a bit sarcastic, sure, but until factuality is explicitly built into the model, I don't think we should use any terminology that implies that the outputs are trustworthy in any way. Until then, every output is like a lucky guess. Similar to a student flipping a coin to answer a multiple choice test. Though they get the correct answer sometimes, it says nothing at all about what they know, or how much we can trust them when we ask a question that we don't already know the answer to. Every LLM user should keep that in mind.
      - chriskanan 3y ago
- insanitybit 3y ago
  That's like saying that I'm hallucinating right now by reading your post and interpreting the words, it just happens to be that I'm reading your post as it is written. Most people call that "thinking".
- justanotherjoe 3y ago
  Just because it inputs and outputs text embeddings, doesnt mean its all text in between. Inside, it doesnt work in units of texts. You wouldnt say a blind human is just a text pattern machine cause it inputs and outputs text. Theres nothing stopping the llm to learn a rich semantic model of the real world in its 100s of billions of params
SrslyJosh 3y ago
What is occurring inside the LLM that differs in these two cases? I don't think that you can demonstrate a difference. The term obscures more than it illuminates.
- tentacleuno 3y ago
  What's different is that for a hallucination, the AI gets it wrong, and for a non-hallucination, the AI got it right.

IshKebab 3y ago

Ah you read [this nonsense](https://news.ycombinator.com/item?id=37874174 https://news.ycombinator.com/item?id=37874174).

Sharlin 3y ago

Do you store "facts"? How can you be sure? Want to prove it for me? Would you like to define "fact" and "language pattern" to me such that the definitions are mutually exclusive?

krainboltgreene 3y ago
This is the most common type of response to any realistic look at LLMs, it's always hilarious. Who are you convincing by using another field of research you also don't understand?
- wongarsu 3y ago
  Of course LLMs don't store facts. They only "experience" the world through text tokens, so at best they can store and process information about text tokens, and any information that can be inferred from those. But that's exactly what philosophy has been arguing about regarding humans since at least Descartes's Evil Demon (the 17th century version of the brain in a vat). Humans don't know anything about "reality", they only know what their senses are telling them. Which is at best a very skewed and limited view of reality, and at worst completely wrong or an illusion. We perceive the world through more facets than an LLM, but fundamentally we share many of their limitations. So if someone says "LLMs don’t store facts", I find "neither do humans" a very reasonable answer, even if its only purpose is to show that "can it store facts" is a bad metric. Of course the more productive part to argue about is the "are facts and language patterns really mutually exclusive", which leads right into "if you had to design an efficient token predictor, would it do 'dumb' math like a markov chain, or would your design include some kind of store of world knowledge? Can you encode a knowledge store in a neural network? And if you can, how can you tell that LLMs don't do that internally?"
  - krainboltgreene 3y ago
    I get that high school philosophy discussions are fun, but it exceptionally weird when it seems to only come up when people doubt the intelligence of a LLM.

spdustin 3y ago

Yes, and in this case, the positional encoding of the tokens used in the system message favored returning them verbatim when asked to return them verbatim.