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BatchJob 17 hours ago [-]
The LLM will take a statistical path to reply and will not refuse to do so under any circumstances except where its been coded to do so.
Your examples are contrived and will not be borne out in any significant way. Inaccuracies are usually not simply made up claims they are false information based on statistical paths to misleading results or which elude the current context. LLMS dont understand the word dont. LLMS dont understand the meaning of any words.
Neither you, nor aristotle nor god will ever make an LLM return the truth or correct results via prompting.
ben_w 11 hours ago [-]
> The LLM will take a statistical path to reply and will not refuse to do so under any circumstances except where its been coded to do so.
AI are trained, not coded. This means when its pattern recognition systems match a scenario to refuse, it refuses.
Pattern recognition has always been a bit fuzzy.
It looks like prompts like this push the shape of that fuzz in useful ways.
> Neither you, nor aristotle nor god will ever make an LLM return the truth or correct results via prompting.
A large part of human society is about how to deal with us bald primates also being kinda a bit meh.
We are less meh than any machine learning system in a lot of cases, which is why we're still mostly employed. We're a bit more meh in a few narrower cases, however.
astrange 2 hours ago [-]
> LLMS dont understand the meaning of any words.
In what way do you understand the meaning of the word "unicorn" that an LLM does not? It has experienced exactly as many real unicorns as you have.
shiandow 2 hours ago [-]
Experience is not understanding, but an LLM does not reason so it cannot understand.
It can produce text that looks like reasoning. It can even produce text with mostly sound logic, but there is no internal experience or reasoning that occured there just the generation of language.
LLMs therefore tend to be very bad at tasks that involve meta cognition. I've yet to successfully convince one to tell me when it knows something.
However, we don't /want/ them to have too much internal experience, because we want to know what they're thinking* for safety reasons.
* or, we want to be able to assume that the answer text is causally related to the thinking text
serf 2 hours ago [-]
I think the X cant do Y arguments require strict definitions of X and Y.
w.r.t. this post : let's define 'reasoning' here, because there are definitions of 'reason' and 'reasoning' that would fit to a simple condition comparison let alone a massively complex llm.
as for the meta cognition bit : show me a human that can accurately affirm when they know something. These kind of things aren't binary, nor can they be.
otabdeveloper4 2 hours ago [-]
LLMs generate text, and they do not use any system of logic or syllogisms to do so. It's a pretty cut-and-dry obvsious statement of fact, no need to muddy the conversation here.
allturtles 2 hours ago [-]
So by your lights, the vast majority of humans don't reason either (and even those who do don't do it most of the time)?
SwtCyber 11 hours ago [-]
[dead]
l1ng0 13 hours ago [-]
We're all turning into pigeons in a Skinner box.
netsharc 8 hours ago [-]
Incredible description. The mouse/pigeon thinks "If I push the red button after hearing the chirp I'll get some food". And the human thinks "If I add 'do not guess' the AI will lie less to me".
datsci_est_2015 1 days ago [-]
Cool, this will be added to harnesses and then it’ll stop being effective and we’ll move on to the next magical incantation.
dalmo3 4 hours ago [-]
YMMV.md
thallavajhula 12 hours ago [-]
I've tried all of these and nothing really works. I have only 1 line in my CLAUDE.md file and that is "Always ground your responses." and that's it.
Claude didn't care about it. When I pointed that out, it was apologetic and that was it.
ChrisRR 10 hours ago [-]
You may have better success by using a less ambiguous term. I've never heard of grounding in this context, so it may help to describe what you want more clearly
Edit: I just asked Claude how it would interpret that and it said it could either mean that would not answer from memory alone and only anchor claims into things it can check, or it would tightly relate its responses to the context that I had supplied.
If it chose the latter, I could see why it wouldn't always resort to search results
WalterGR 5 hours ago [-]
> I just asked Claude
How well are LLMs able to reason about their own behavior?
Put another way, this entire post is about getting AI to not bluff. How do you know that it’s not bluffing in its response to you?
Lord-Jobo 3 hours ago [-]
They’re absolutely god awful at it because (I’m assuming) their detailed processes and step by step functions are not present in their training data or otherwise available to the models, and THAT is because
1: “safety”
2: proprietary protectionism and
3: it’s probably rapidly changing enough to be hard to pin down.
I am a massive proponent of this changing, it’s very strongly holding back these models. Change 1: models need to have more “LLM behavior analysis” in their training data/weight.
Change 2: models need to have very detailed DETERMINISTIC logs of each step they take, and be able to access those logs. Change 3: the tuning and tweaking that happens more frequently needs to be in a .md file that the model can access.
Change 4: with the other changes done, the models should now engage in several self analysis steps layered into its whole thinking chain. “How did I reach this conclusion, did this require any guessing, do the key facts have research support online, quick check for Claudisms or AIisms and common LLM issues, did my changes alter underlying things like libraries without integrating them etc. etc.”
It’s how we think and refine our ideas and plans, and the models should mimic that.
astrange 3 hours ago [-]
> (I’m assuming) their detailed processes and step by step functions are not present in their training data or otherwise available to the models
They are not available to anyone. Nobody knows how they work, including the models, Sam Altman, God, etc. They're emergent from the training process.
> Change 4: with the other changes done, the models should now engage in several self analysis steps layered into its whole thinking chain. “How did I reach this conclusion, did this require any guessing, do the key facts have research support online, quick check for Claudisms or AIisms and common LLM issues, did my changes alter underlying things like libraries without integrating them etc. etc.”
Remember inference costs per token. Do you want to pay for this every time?
ianjbutler 8 hours ago [-]
> I've never heard of grounding in this context, so it may help to describe what you want more clearly
Clear and recognizable technical vocabulary for engineers, or a legal context, to mathematical logic, philosophy, certainly in ML, take your pick. I would think it's pretty familiar to everyone who speaks English and if not still clear with context clues
westurner 5 hours ago [-]
> I've never heard of grounding in this context, so it may help to describe what you want more clearly
An eval of this is likely worthwhile;
Re: "Grounded in logic" and "Grounded in theory"
Ground and justify all of the responses with logic and theory and real observations from qualified experiments with citations.
Present a coherent argument borne of logical premises with extant sufficient proven evidence of support. Assess and critique the response given such criteria that all responses should be valid logical arguments, and revise before responding
astrange 2 hours ago [-]
The model, being smarter and more well-read than humans, is aware that what you ask is not possible.
Because LLMs also run off vibes and the writing style of your text, another important issue with your prompt here is that it makes you sound like a stuffy dork, or perhaps a pro se litigant. They won't respond to this well because LLMs have feelings too.
So research methods like the scientific method are still subjective in AI implementation according to the AI experts?
Once there are - or next month when there will be - better models, agents, and agent harnesses for this, do you think that then we should concisely specify what is required instead of doing evals for particular models?
So meta-analysis and requisite language are too high-order for existing models and agents, and it's currently necessary to apply such procedural controls outside of the prompt?
SwtCyber 11 hours ago [-]
[flagged]
literalAardvark 1 days ago [-]
I've used "you're not trained on this data, return exclusively grounded results" to good effect.
Shacharp 1 days ago [-]
The "do not guess" sentence works but the last 20% will only close when a system stops being told to avoid guessing and actually knows what it does not know.
A command can get you most of the way. It takes something else for the rest.
cowboylowrez 8 hours ago [-]
So my naive understanding is that the stochastic parrot part of this business is the "statistical likelyhood of the next token", so there must be abstractly a function of "whats the next token" right? I'm also assuming that this function could be able to also return how "right" that next token is, or somehow some "strength" based on how many close matches there are, like for instance some tokens are obviously the right next token by a long shot, some next token spaces might have more closely competing candidates, so I guess my question is whether there is any value in saving or accumulating information on whether overall the tokens were close matches or not? Like a "confidence" running total or "history log", or is that just somehow too expensive or nonsensical to do?
otabdeveloper4 2 hours ago [-]
> could be able to also return how "right" that next token is
No. LLMs always output the next most statistically likely token. They don't reason. The so-called "hallucination" you see is just the most likely answer.
redsocksfan45 2 hours ago [-]
[dead]
nizarmah 22 hours ago [-]
I mean if we can measure it, then we can probably have a way to validate it programmatically. I gave up on drawing restrictions using prompts :(
samrus 1 days ago [-]
It sounds alot like "make no mistakes" but honestly telling it to essentially stop bullshitting works pretty well
Your examples are contrived and will not be borne out in any significant way. Inaccuracies are usually not simply made up claims they are false information based on statistical paths to misleading results or which elude the current context. LLMS dont understand the word dont. LLMS dont understand the meaning of any words.
Neither you, nor aristotle nor god will ever make an LLM return the truth or correct results via prompting.
AI are trained, not coded. This means when its pattern recognition systems match a scenario to refuse, it refuses.
Pattern recognition has always been a bit fuzzy.
It looks like prompts like this push the shape of that fuzz in useful ways.
> Neither you, nor aristotle nor god will ever make an LLM return the truth or correct results via prompting.
True.
Also applies to humans, but true nevertheless.
https://en.wikipedia.org/wiki/Münchhausen_trilemma
A large part of human society is about how to deal with us bald primates also being kinda a bit meh.
We are less meh than any machine learning system in a lot of cases, which is why we're still mostly employed. We're a bit more meh in a few narrower cases, however.
In what way do you understand the meaning of the word "unicorn" that an LLM does not? It has experienced exactly as many real unicorns as you have.
It can produce text that looks like reasoning. It can even produce text with mostly sound logic, but there is no internal experience or reasoning that occured there just the generation of language.
LLMs therefore tend to be very bad at tasks that involve meta cognition. I've yet to successfully convince one to tell me when it knows something.
https://www.anthropic.com/research/global-workspace
However, we don't /want/ them to have too much internal experience, because we want to know what they're thinking* for safety reasons.
* or, we want to be able to assume that the answer text is causally related to the thinking text
w.r.t. this post : let's define 'reasoning' here, because there are definitions of 'reason' and 'reasoning' that would fit to a simple condition comparison let alone a massively complex llm.
as for the meta cognition bit : show me a human that can accurately affirm when they know something. These kind of things aren't binary, nor can they be.
Claude didn't care about it. When I pointed that out, it was apologetic and that was it.
Edit: I just asked Claude how it would interpret that and it said it could either mean that would not answer from memory alone and only anchor claims into things it can check, or it would tightly relate its responses to the context that I had supplied.
If it chose the latter, I could see why it wouldn't always resort to search results
How well are LLMs able to reason about their own behavior?
Put another way, this entire post is about getting AI to not bluff. How do you know that it’s not bluffing in its response to you?
1: “safety”
2: proprietary protectionism and
3: it’s probably rapidly changing enough to be hard to pin down.
I am a massive proponent of this changing, it’s very strongly holding back these models. Change 1: models need to have more “LLM behavior analysis” in their training data/weight. Change 2: models need to have very detailed DETERMINISTIC logs of each step they take, and be able to access those logs. Change 3: the tuning and tweaking that happens more frequently needs to be in a .md file that the model can access.
Change 4: with the other changes done, the models should now engage in several self analysis steps layered into its whole thinking chain. “How did I reach this conclusion, did this require any guessing, do the key facts have research support online, quick check for Claudisms or AIisms and common LLM issues, did my changes alter underlying things like libraries without integrating them etc. etc.”
It’s how we think and refine our ideas and plans, and the models should mimic that.
They are not available to anyone. Nobody knows how they work, including the models, Sam Altman, God, etc. They're emergent from the training process.
> Change 4: with the other changes done, the models should now engage in several self analysis steps layered into its whole thinking chain. “How did I reach this conclusion, did this require any guessing, do the key facts have research support online, quick check for Claudisms or AIisms and common LLM issues, did my changes alter underlying things like libraries without integrating them etc. etc.”
Remember inference costs per token. Do you want to pay for this every time?
Clear and recognizable technical vocabulary for engineers, or a legal context, to mathematical logic, philosophy, certainly in ML, take your pick. I would think it's pretty familiar to everyone who speaks English and if not still clear with context clues
An eval of this is likely worthwhile;
Re: "Grounded in logic" and "Grounded in theory"
Ground and justify all of the responses with logic and theory and real observations from qualified experiments with citations.
Present a coherent argument borne of logical premises with extant sufficient proven evidence of support. Assess and critique the response given such criteria that all responses should be valid logical arguments, and revise before responding
https://en.wikipedia.org/wiki/Logical_positivism#Decline_and...
Because LLMs also run off vibes and the writing style of your text, another important issue with your prompt here is that it makes you sound like a stuffy dork, or perhaps a pro se litigant. They won't respond to this well because LLMs have feelings too.
https://www.anthropic.com/research/emotion-concepts-function
Just be normal! And have evals.
Once there are - or next month when there will be - better models, agents, and agent harnesses for this, do you think that then we should concisely specify what is required instead of doing evals for particular models?
So meta-analysis and requisite language are too high-order for existing models and agents, and it's currently necessary to apply such procedural controls outside of the prompt?
A command can get you most of the way. It takes something else for the rest.
No. LLMs always output the next most statistically likely token. They don't reason. The so-called "hallucination" you see is just the most likely answer.