61–70 of 149 posts
If “allowing the execution of arbitrary database queries written by an LLM inside a SaaS application” is the answer, I’d love to know what the question is.
The question is how to make money from LLM hype.
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You cannot fix what you cannot measure, here is an attempt to do just that with HallMeter https://why.network/ Still have to figure a measurement unit.
How about "falsehood quotient"? Count the number of counterfactual assertions in a given text, then divide by the number of sentences. Of course, the question of what is a falsehood is an exercise for the reader, but this would at least give a unit of measurement, flawed as it is.
Second, it isn’t even necessarily better to have fewer lies if those few lies are more subtle. Plenty of propaganda works by twisting facts and using misleading statements. Perhaps the worst offenders won’t even have any outright falsehoods at all.
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"Hallucination" makes it sound like ChatGPT drank some of the punch without realizing it was laced with LSD. "Bullshit" sounds more like what comes out of an overconfident ass who should or could know better with some better education.
"Confabulation" is the correct and precise term that comports with the English language, rather than being jargon requiring a neologism.
And why would that be? "Hallucination" means "erratic wandering", implying one is lost - similarly to "delirium" (maetaphor using the plough) and "error". Part of the idea is that of "instead of witnessing the correct, reporting the false" - a very ancient, traditional idea, and akin to the concept of "intelligence" (intus-legere).
"Confabulation" means locutor and interlocutor are talking, exchanging narrations.
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"Hallucination" makes it sound like ChatGPT drank some of the punch without realizing it was laced with LSD. "Bullshit" sounds more like what comes out of an overconfident ass who should or could know better with some better education.
Hallucination is a better descriptor for what an LLM is doing though. A bullshitter knows they don't know, an LLM just strings words together in ways that fit what it "saw" from training data. IMO the main problem with calling them hallucinations is the implication that the true things they say are true on purpose. It's hallucinating the true things too.
(Issue is, now some are convinced that people in general would do the same and just blurt out the feedforward output of their "internal neural network", as opposed to having built knowledge in a loop of critical evaluation.)
'Facts' aren't as black and white as people think. "What does Charmander evolve into?" "What does the spell 'avada kedavra' do?" "What is the Sindarin word for 'friend'?" "What are the names of Santa's reindeer?" "Where did Robin Hood live?" "Where did Achilles die?" These are all 'factual questions' you can find answers to from reputable sources like Wikipedia. Google displays 'fact boxes' for several of them. Wolfr…
There also exists the consideration of allographemical contextualization, the nature of relevance, pragmatics, conjunct identification of context, semantics. To be honest the linguistics side alone is vast. Knowledge and cognition however. . . A whole other ballgame. But the only tool we have to really get down to the bottom of how knowledge works is language, it's to epistemological pursuit what math is to physics.
While GPT is super impressive and can do a lot of quasi-brute-force things, we're only finding now the rudiments of the machined intelligence paradigm, and it will behoove any reader to brush up on their classics, true pursuants of philosophy and many order logic are about to be in high demand if I had to reckon.
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> When an LLM is suggesting what might come next in a piece of text... it doesn't know if it's supposed to guess a probable word from a Wikipedia article, an Onion article, a Project Gutenberg manuscript, or an Archive Of Our Own fanfic. The obvious start seems to be having separate fiction and nonfiction LLMs and not training the nonfiction ones on Archive Of Our Own. People also end up confused about the truth when…
But there's a fundamental issue here. The real strength of LLMs is not just information retrieval, but being able to dynamically recombine that information. Of course that's also their weakness. The reason GPT will regularly produce code with nonexistent API calls is not because it's been trained on 'fictional APIs', but because it's combining various real calls to make new fictional ones. The obvious answer then is…
It is not a good start that they begin with a dictionary definition of Hallucinations. While the similarities to what a LLM does are apparent enough for the term to be used, LLMs are under no obligation to behave similar to the dictionary definition of Hallucinations. In general facts are not the answer to Hallucinations. You can't possibly have every fact for every situation. The true solution to Hallucinations is f…
This article suggests that LLMs should use a database as a reference for factual information. Rather than asking LLMs to provide their own answers, it is recommended that they summarize based on the facts extracted from the database. This approach reduces the likelihood of hallucinations among LLMs.
What they are literally doing is guessing the next word, a word a time but doing it really really well and making statistically average output over a very large number of inputs.
There is no distinction between understanding "the" vs "a" and telling me 1+1=3. It is all token generation.
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This article suggests that LLMs should use a database as a reference for factual information. Rather than asking LLMs to provide their own answers, it is recommended that they summarize based on the facts extracted from the database. This approach reduces the likelihood of hallucinations among LLMs.
We already had databases of facts, like Wolfram Alpha, decades before LLM, and we largely ignored them. It's ironic that when trying to solve AI problems we keep reverting to these old patterns we've tried since the 80s and they kept failing. Habits die hard, I guess. There's a categorical difference between knowing a fact, and looking up a fact. When you know a fact you can recognize it in a situation where you woul…