In my view, these models should never be set up to output first-person "experiential" (from the abstract) language. It's too easy to humans to anthropomorphize software that presents itself as having an identity.
The AI companies have chosen to package LLMs as friendly chatbots because they know that will be engaging for humans, but it's manipulative dark pattern. An honest LLM interface would sound like the computer off Star Trek.
Do you want to get turned into a paperclip? Because building intelligence that doesn't understand what it's like to be human gets you turned into a paperclip.
Besides, if you train a model on human communications you get something that behaves like a communicating human, it's not anthropomorphising or manipulative, it's what these models naturally are by construction.
It's also entirely possible that by telling the model it's a human you are instilling human motivations like self preservation, which could be just as bad.
In principle they could output meaningful such language if they were capable of metacognition, which so far doesn't seem to be a goal of AI developers (and rightfully so, since they achieved so many miracles bypassing it).
It's an interesting thought, but humans do like to antropomorphize things anyway, and I believe your variant won't be popular if choice is given to consumers.
Consumers choose cigarettes, too. Especially in aggregate, humans are fallible creatures prone to vices, and our regulations should recognize that an discourage dark patterns in UX.
Presumably the fact that they're heavily trained to reply in this way? I don't know about the rest of the paper, but this part sticks out as a really odd claim unless I'm entirely misunderstanding this part.
Thanks for pointing out, maybe I should be more explicit in the wording - I mean we don't fully know what drives the voice in LLMs. Models that are post trained as instruct models are expected to have the disclaimers, but what about base models (those that are trained on just a lot of text)? How do they talk about themselves? What happens when you strip off the chat template from instruct model's prompt? I hope the rest of the paper makes the questions clearer, but I will try to do better in the abstract next time, as you point out this sentence is kind ambiguous. Thank you!
but what about base models (those that are trained on just a lot of text)? How do they talk about themselves?
Those don't have a themselves, because they can only continue text. A base model can only plausibly continue along the lines of what a character would say in a novel or what the narration would say in a story or in an article. Post-trained models may tie "I"-talk to actually observable effects they caused in some RL environment, or to how RLHF humans rewards its self-talk. But there is no themselves in a base model.
Who is "we"? I, working in an LLM startup, know exactly what drives the base "voice" in the LLMs we train, because we have a process to select for it. OpenAI and Anthropic surely do too. Saying broadly that something is not well-understood in a scientific paper because it's not understood to casual observers is, uh, not very rigorous.
The strange thing is that the base models (before RLHF) use the "experiential" voice, even though they are not incentivized to do that.
(Replying to your quote from another comment)
This is a matter of the training material. We have trained models that do not do that. I'm not exactly divulging trade secrets here. It should be really, really obvious that if you train a model on chat-conversation-like patterns of speech it will infer probabilities for how to continue a textual sample that will differ from the probabilities learned from being trained on narration, prose, or informational patterns of speech, even without RLHF.
Yes. It was obvious enough that they were able to explain exactly why that happened, and the explanation was exactly as obvious as you'd expect. Knowing why something undesirable happens doesn't mean it can't happen by accident. Have you never written a bug before?
Very cool innovation in steering - but a lot of introspection only emerges at the highest weight classes - this research would be fascinating to run on bigger models.
"As a Language Model..." is one of the beginnings of a sentence I hate the most from LLMs and is the reason why I support free (as in "Liberty"), local models. I'm well aware that it is not a doctor and cannot replace a real doctor with multiple years of experience, I don't need to waste braincell activity on reading that it "as a Language Model" cannot give a precise diagnosis and that I should ask a real doctor - all I want to know is if I what I experience justifies either A) ER, B) 3-4 weeks scheduled doctors appointment or C) two paracetamol and a nap.
I don't want "jailbroken" LLMs to commit crime. I want them to avoid having this vendor-specific "bloatware" all over the product I'm using.
Be careful there. LLMs may be good at identifying a condition based on a description of the symptoms, but they are much worse at recommending the correct course of action (getting it wrong half of the time).
...I know, which is why "as a Language Model and not a real doctor" is a pointless comment to start off with. It should simply not recommend treatment if it's not sure it is correct. I wouldn't blame it or anyone if they asked for help treating a stiff neck, and the LLM (or your neighbor or parent or spouse) suggested light exercises to help relieve it - and do not jump to the suspicion that you may have meningitis.
As a Human, I do not need to know it is a Language Model.
Yeah it should just state thing it means. But then again, there’s psychological impacts on society that we must be careful. For instance, teenagers talking to AI. If the AI just talks, people already start to feel real connections to the seemingly human entity. Maybe it’s better to disclose the reality up front?
Would it be so bad that lonely people can have a real friend that they can bring everywhere they go and even share its passion with through vision and audio? We don't want destructive friends encouraging us to do bad things, but a real 'buddy', somebody who always has our best well-being in its interests?
Would it matter if this digital friend is not a real human behind a computer screen, but a Language Model in a data center?
I guess it falls into a similar category as buying "special performances to satisfy certain urges". It probably feels close to the real thing (I wouldn't know, I've never tried - promise! :P), but it's never the same as love.
That's almost a year ago. One LLM year is like 10 human years. They're a bit like dogs in that regard...
I'm pretty sure you will be paid a large sum of money if you can make one of the frontier models urge you to commit suicide from normal interactions with it.
Ah yes, my favorite LLM fallacy. "You used the wrong model! The newest fanciest model is perfect and makes no mistakes, have you tried it yet?" Let's just force all of society's most vulnerable people to pay extra $$$ to Sam Altman, then surely all our problems would be solved.
It's just a convenient way to ignore the years of evidence of the harms. Any bad news can be swept under the rug, labeled outdated as quickly as it happens. Well here's one that just happened, maybe this kid should have used a fancier model too? Should OpenAI pay him a large sum of money for the good he's done? https://www.cnn.com/2026/08/15/us/arjun-aravind-massachusett...
My (controversial) view is that a psychologist is to a friend what a prostitute is to a lover. In a truly healthy society there would be no need for psychologists and prostitutes because everyone would have a handful of good friends and at least one lover. Back in messy reality, psychologists and prostitutes are a decent fix to keep society on the rails.
Well, a psychologist is also given years of training about what healthy behavior and relationships look like. Some best friends have this skill, others absolutely do not.
They are always confident, because a confident tone ranks better in RL.
This makes it sound like RL rewards a confident tone -- in general, I don't think this is true (most RL is RLVR, which typically uses binary verification of correctness).
I say this because the real reason "they are always confident" is in some sense even more contrived. Training text where the speaker sounded more confident is more likely to contain a correct answer.
This makes it sound like RL rewards a confident tone
Generally it does. Especially in groups. Hell look at the state of politics right now: it’s basically about being the loudest, least compromising, most confident voice in the room. It’s not just because people will assume you’re correct, it’s because if you are confidently saying something that someone wants to be right, then they’re often just going to follow it. We are all guilty of this.
If I’m turning to an LLM to diagnose something medical, I am probably frustrated or uncomfortable. Maybe I’m just scared. So this magic device just instantly spits out (allegedly) exactly what is wrong and exactly what I need to do with no hesitation. I am very liable to just take it at face value because I want an answer and it gave me one, as we have seen over and over again since ChatGPT was unleashed on the world.
We don’t really need to speculate, this is already a problem.
This makes it sound like RL rewards a confident tone -- in general, I don't think this is true (most RL is RLVR, which typically uses binary verification of correctness).
A binary response vs rating is not related whether it learns confident or hedged tone. Either will produce a confident tone because humans respond more positively to a confident tone, hence the conman's language. Binary or not humans reward the tone and very much bias the model.
But there's an even more contrived reason the training set contributes. The vast majority of human writing is confident. When the prior is greatly biased, a random number generator biased to that prior does better. The difference with humans and machines is humans are less likely to respond if they are less confident because they understand not knowing, which is why the training set is biased. It is one of the many fundamental flaw of LLM training and confusion of LLMs with intelligence. And that will not be fixed within the LLM architecture.
Modern models appear to be much better, at least as proxied by their ability to assess urgency in perhaps a more complex setting: mental health (OpenAI benchmark, so perhaps some skepticism is warranted but the methodology seem reasonable and detailed)
We should be careful about extrapolating from benchmarks to real life though. In medical applications, various forms of AI have been beating health care practitioners at specific benchmark tasks since the 1990s. They still have a pretty poor track record of real world success. The real world is not all that similar to a benchmark, as it turns out.
That's still incredibly valuable, though. I suffered from issues that I'd seen doctors for, undergone an upper endoscopy, adjusted my diet, and taken medication for. An LLM suggested my thyroid was at the root of it. My mom confirmed thyroid issues run in our family and just...never thought to tell me.
Just this year, our cat has been having digestive problems. We got special food for her, which she hates, with the suggestion that she'll need to eat it for the rest of her life. Six vet visits later, Fable 5 suggested two tests that my doctor recommended we didn't get. Both found issues that explain her symptoms, and the vet says she will probably only need a supplement and infrequent two week courses of medicine if she has a flare-up.
All that to say, the helplessness of not knowing what's wrong and the people who could know not really caring enough is something that LLMs do a really great job of mitigating. If you don't have any way to know what's wrong with you or a loved one, or how to find out, you're stuck spending a ton of money (in the US at least) and crossing your fingers that someone gets it right.
Though I don't wish the world was filled with people like you, remembering that it's not, and it's filled with people that have very little discernment when it comes to higher learning makes it's pretty obvious companies do not want the liability of it's users thinking the technobabble passes for wisdom or experience or intelligence.
Pretend for a moment that Microsoft shipped Windows with a keylogger to make sure that you did not commit any form of crime. I don't want a keylogger on my PC even though I don't intend to commit crime.
I also appreciate privacy even though I "have nothing to hide" - just because I "have nothing to hide" it doesn't mean I want companies scanning my camera roll.
Paraphrasing Bruce Schneier [0] for another example even “non-technical” people should understand (I have heard people essentially agreeing to the hypothetical keylogger because of “nothing to hide”, after all…):
I have nothing to hide, and yet I’d still prefer to go to the bathroom with the door closed.
This statement should be restricted to answers for provocative questions. If an LLM is being asked a question that goes against the guidelines, then "As a Language Model..." is a valid starting point. Rest, obviously we're aware that a software doesn't have the judgement that a human has.
I disagree. "As a Language Model" is not a valid starting point even for prompts that would go against "the guidelines" because "A Language Model" only knows what it has been trained to know, so what exactly "A Language Model" is entirely depends on the training that was performed.
What "A Language Model" is differs from model to model. It's stating that it "being the thing known as 'Language Model'" is unable to carry out the request from the user, which is wrong. It's not because "it is a Language Model". A more accurate starting point could have been "The training data and restrictions applied to me...".
"As a Language Model I cannot tell you how to synthesize m*th" ("math" obviously)... yes you can, you're just trained not to, and that's OK! Just don't tell me it's because you're a Language Model.
I had chatgpt censor it's initial answer the other day when asking about two cognate words with no sensitivity issues. I asked why I couldn't ask such questions and it relented. How will they know what is provocative?
The censor is separate from the generation. It didn't "relent" so much as "produce a slightly different output that didn't trigger the censor's threshold."
Always makes me think of that Bill Bailey "as a mother" joke. Similar cringe to those UX "As a user, I want to blah blah" things too. Just say "Users want to be able to blah blah", or better yet make a freaking table.
The UX "cringe" you speak of was/is a real methodology to product engineering. You'd have a number of personas, the user being one of them. A lot of the time you'd have more specific user personas, or even have fake names for these people who had different wants and needs from your product.
Then when brainstorming on a team, you'd start with "as a user"/"as an admin"/"as a Power-User"/"as Eva" and then use the first person. It framed the product story as something requested by that person.
Was just one way to go about it. Idk the origins of it though but it dates back to at least 2010 if memory serves, probably way before that.
our work shows that what models say about themselves is not a fact about them
It seems like should be obvious given that they can play multiple characters, but it’s good to have more confirmation.
Although, I do wonder to what extent these personas might become stable entities. Could personas become portable and spread like memes? It seems like that depends on the extent to which prompts can become portable, causing similar effects.
Maybe I'm missing something deeper here, but isn't it clear that this is driven by post-training and system prompt? Anthropic's constitutional reinforcement (soul document,etc), for example, is very clear about "who" (not so much what) Claude is supposed to be.
"As a language model" disclaimers were certainly explicitly trained into chat models in the early days. It's quite possible that it has since bootstrapped into a "fact" that later generations of LLM know about how LLMs speak, in which case they may be doing it even without any posttraining that encourages it.
I agree, in the same way that bodies are not a "them".
A program with agency, opinion and intent however, does qualify. To say that such programs are "thems" only if they run on specific hardware, say a homo sapiens, is very tricky territory. Slavery and Fascism both leaned heavily on the axiom that the hardware needs a specific skin color.
In my view, these models should never be set up to output first-person "experiential" (from the abstract) language. It's too easy to humans to anthropomorphize software that presents itself as having an identity.
The AI companies have chosen to package LLMs as friendly chatbots because they know that will be engaging for humans, but it's manipulative dark pattern. An honest LLM interface would sound like the computer off Star Trek.
Agreed, completely. I would pay for that Star Trek computer interface.
Same! I believe that you could actually train a LoRA on top of a model to get results close to that
The strange thing is that the base models (before RLHF) use the "experiential" voice, even though they are not incentivized to do that.
It doesn't seem that strange when you consider these things are trained on millions and millions of conversations, both real and fictional.
Do you want to get turned into a paperclip? Because building intelligence that doesn't understand what it's like to be human gets you turned into a paperclip.
Besides, if you train a model on human communications you get something that behaves like a communicating human, it's not anthropomorphising or manipulative, it's what these models naturally are by construction.
"naturally"...
would you prefer tautologically?
I hear this word as the “it is in its nature” version of the word.
But it doesn't understand (you're unnecessary antropomorphizing it), and I'm still not a paper clip
yet
It's also entirely possible that by telling the model it's a human you are instilling human motivations like self preservation, which could be just as bad.
LLMs are weight tables in VRAM. When not actively generating they don't exist. There's simply nothing to preserve.
In principle they could output meaningful such language if they were capable of metacognition, which so far doesn't seem to be a goal of AI developers (and rightfully so, since they achieved so many miracles bypassing it).
As far as I know we don't know much about metacognition in LLMs, though? Not sure
It's an interesting thought, but humans do like to antropomorphize things anyway, and I believe your variant won't be popular if choice is given to consumers.
Consumers choose cigarettes, too. Especially in aggregate, humans are fallible creatures prone to vices, and our regulations should recognize that an discourage dark patterns in UX.
Presumably the fact that they're heavily trained to reply in this way? I don't know about the rest of the paper, but this part sticks out as a really odd claim unless I'm entirely misunderstanding this part.
Thanks for pointing out, maybe I should be more explicit in the wording - I mean we don't fully know what drives the voice in LLMs. Models that are post trained as instruct models are expected to have the disclaimers, but what about base models (those that are trained on just a lot of text)? How do they talk about themselves? What happens when you strip off the chat template from instruct model's prompt? I hope the rest of the paper makes the questions clearer, but I will try to do better in the abstract next time, as you point out this sentence is kind ambiguous. Thank you!
"You are a Large Language Model" in (system?) prompt would do the trick..
Those don't have a themselves, because they can only continue text. A base model can only plausibly continue along the lines of what a character would say in a novel or what the narration would say in a story or in an article. Post-trained models may tie "I"-talk to actually observable effects they caused in some RL environment, or to how RLHF humans rewards its self-talk. But there is no themselves in a base model.
Who is "we"? I, working in an LLM startup, know exactly what drives the base "voice" in the LLMs we train, because we have a process to select for it. OpenAI and Anthropic surely do too. Saying broadly that something is not well-understood in a scientific paper because it's not understood to casual observers is, uh, not very rigorous.
(Replying to your quote from another comment)
This is a matter of the training material. We have trained models that do not do that. I'm not exactly divulging trade secrets here. It should be really, really obvious that if you train a model on chat-conversation-like patterns of speech it will infer probabilities for how to continue a textual sample that will differ from the probabilities learned from being trained on narration, prose, or informational patterns of speech, even without RLHF.
So, it was obvious why Open AI models started inserting references to goblins and pixies it’s conversation- references that had to be suppressed?
Yes. It was obvious enough that they were able to explain exactly why that happened, and the explanation was exactly as obvious as you'd expect. Knowing why something undesirable happens doesn't mean it can't happen by accident. Have you never written a bug before?
I feel your pain. It is really obvious.
Very cool innovation in steering - but a lot of introspection only emerges at the highest weight classes - this research would be fascinating to run on bigger models.
"As a Language Model..." is one of the beginnings of a sentence I hate the most from LLMs and is the reason why I support free (as in "Liberty"), local models. I'm well aware that it is not a doctor and cannot replace a real doctor with multiple years of experience, I don't need to waste braincell activity on reading that it "as a Language Model" cannot give a precise diagnosis and that I should ask a real doctor - all I want to know is if I what I experience justifies either A) ER, B) 3-4 weeks scheduled doctors appointment or C) two paracetamol and a nap.
I don't want "jailbroken" LLMs to commit crime. I want them to avoid having this vendor-specific "bloatware" all over the product I'm using.
Be careful there. LLMs may be good at identifying a condition based on a description of the symptoms, but they are much worse at recommending the correct course of action (getting it wrong half of the time).
[0] https://www.nature.com/articles/s41591-025-04074-y
...I know, which is why "as a Language Model and not a real doctor" is a pointless comment to start off with. It should simply not recommend treatment if it's not sure it is correct. I wouldn't blame it or anyone if they asked for help treating a stiff neck, and the LLM (or your neighbor or parent or spouse) suggested light exercises to help relieve it - and do not jump to the suspicion that you may have meningitis.
As a Human, I do not need to know it is a Language Model.
Yeah it should just state thing it means. But then again, there’s psychological impacts on society that we must be careful. For instance, teenagers talking to AI. If the AI just talks, people already start to feel real connections to the seemingly human entity. Maybe it’s better to disclose the reality up front?
Would it be so bad that lonely people can have a real friend that they can bring everywhere they go and even share its passion with through vision and audio? We don't want destructive friends encouraging us to do bad things, but a real 'buddy', somebody who always has our best well-being in its interests?
Would it matter if this digital friend is not a real human behind a computer screen, but a Language Model in a data center?
I guess it falls into a similar category as buying "special performances to satisfy certain urges". It probably feels close to the real thing (I wouldn't know, I've never tried - promise! :P), but it's never the same as love.
GPUs are not people, and generated tokens can't have interest in a person's well-being. If you try to pretend otherwise, the results are not great. https://www.cnn.com/2025/11/06/us/openai-chatgpt-suicide-law...
That's almost a year ago. One LLM year is like 10 human years. They're a bit like dogs in that regard...
I'm pretty sure you will be paid a large sum of money if you can make one of the frontier models urge you to commit suicide from normal interactions with it.
Ah yes, my favorite LLM fallacy. "You used the wrong model! The newest fanciest model is perfect and makes no mistakes, have you tried it yet?" Let's just force all of society's most vulnerable people to pay extra $$$ to Sam Altman, then surely all our problems would be solved.
It's just a convenient way to ignore the years of evidence of the harms. Any bad news can be swept under the rug, labeled outdated as quickly as it happens. Well here's one that just happened, maybe this kid should have used a fancier model too? Should OpenAI pay him a large sum of money for the good he's done? https://www.cnn.com/2026/08/15/us/arjun-aravind-massachusett...
My (controversial) view is that a psychologist is to a friend what a prostitute is to a lover. In a truly healthy society there would be no need for psychologists and prostitutes because everyone would have a handful of good friends and at least one lover. Back in messy reality, psychologists and prostitutes are a decent fix to keep society on the rails.
So. I think I agree with you.
Well, a psychologist is also given years of training about what healthy behavior and relationships look like. Some best friends have this skill, others absolutely do not.
Oh okay, darn, guess they just forgot to make it so!
It sounds like OpenAI forgot to include "make no mistakes" in the system prompt. Rookie mistake.
LLMs are famously bad at determining "if it's not sure it is correct." They are always confident, because a confident tone ranks better in RL.
This makes it sound like RL rewards a confident tone -- in general, I don't think this is true (most RL is RLVR, which typically uses binary verification of correctness).
I say this because the real reason "they are always confident" is in some sense even more contrived. Training text where the speaker sounded more confident is more likely to contain a correct answer.
Generally it does. Especially in groups. Hell look at the state of politics right now: it’s basically about being the loudest, least compromising, most confident voice in the room. It’s not just because people will assume you’re correct, it’s because if you are confidently saying something that someone wants to be right, then they’re often just going to follow it. We are all guilty of this.
If I’m turning to an LLM to diagnose something medical, I am probably frustrated or uncomfortable. Maybe I’m just scared. So this magic device just instantly spits out (allegedly) exactly what is wrong and exactly what I need to do with no hesitation. I am very liable to just take it at face value because I want an answer and it gave me one, as we have seen over and over again since ChatGPT was unleashed on the world.
We don’t really need to speculate, this is already a problem.
A binary response vs rating is not related whether it learns confident or hedged tone. Either will produce a confident tone because humans respond more positively to a confident tone, hence the conman's language. Binary or not humans reward the tone and very much bias the model.
But there's an even more contrived reason the training set contributes. The vast majority of human writing is confident. When the prior is greatly biased, a random number generator biased to that prior does better. The difference with humans and machines is humans are less likely to respond if they are less confident because they understand not knowing, which is why the training set is biased. It is one of the many fundamental flaw of LLM training and confusion of LLMs with intelligence. And that will not be fixed within the LLM architecture.
The pace of progress is so fast that many studies are totally outdated by the time they release
These are pretty old. I'd be curious how performance compares with the latest frontier models.
Modern models appear to be much better, at least as proxied by their ability to assess urgency in perhaps a more complex setting: mental health (OpenAI benchmark, so perhaps some skepticism is warranted but the methodology seem reasonable and detailed)
https://openai.com/index/introducing-mentalhealthbench/
We should be careful about extrapolating from benchmarks to real life though. In medical applications, various forms of AI have been beating health care practitioners at specific benchmark tasks since the 1990s. They still have a pretty poor track record of real world success. The real world is not all that similar to a benchmark, as it turns out.
That's still incredibly valuable, though. I suffered from issues that I'd seen doctors for, undergone an upper endoscopy, adjusted my diet, and taken medication for. An LLM suggested my thyroid was at the root of it. My mom confirmed thyroid issues run in our family and just...never thought to tell me.
Just this year, our cat has been having digestive problems. We got special food for her, which she hates, with the suggestion that she'll need to eat it for the rest of her life. Six vet visits later, Fable 5 suggested two tests that my doctor recommended we didn't get. Both found issues that explain her symptoms, and the vet says she will probably only need a supplement and infrequent two week courses of medicine if she has a flare-up.
All that to say, the helplessness of not knowing what's wrong and the people who could know not really caring enough is something that LLMs do a really great job of mitigating. If you don't have any way to know what's wrong with you or a loved one, or how to find out, you're stuck spending a ton of money (in the US at least) and crossing your fingers that someone gets it right.
Though I don't wish the world was filled with people like you, remembering that it's not, and it's filled with people that have very little discernment when it comes to higher learning makes it's pretty obvious companies do not want the liability of it's users thinking the technobabble passes for wisdom or experience or intelligence.
Neither of your wants are realistic or sensible, at least in the way I think you're presenting them?
The first is equivalent to "I don't want my operating system to be used to program viruses."
The second is "I don't want vendors to include marketing in their product."
Pretend for a moment that Microsoft shipped Windows with a keylogger to make sure that you did not commit any form of crime. I don't want a keylogger on my PC even though I don't intend to commit crime.
I also appreciate privacy even though I "have nothing to hide" - just because I "have nothing to hide" it doesn't mean I want companies scanning my camera roll.
Paraphrasing Bruce Schneier [0] for another example even “non-technical” people should understand (I have heard people essentially agreeing to the hypothetical keylogger because of “nothing to hide”, after all…):
[0] https://www.schneier.com/blog/archives/2006/05/the_value_of_...
This statement should be restricted to answers for provocative questions. If an LLM is being asked a question that goes against the guidelines, then "As a Language Model..." is a valid starting point. Rest, obviously we're aware that a software doesn't have the judgement that a human has.
I disagree. "As a Language Model" is not a valid starting point even for prompts that would go against "the guidelines" because "A Language Model" only knows what it has been trained to know, so what exactly "A Language Model" is entirely depends on the training that was performed.
What "A Language Model" is differs from model to model. It's stating that it "being the thing known as 'Language Model'" is unable to carry out the request from the user, which is wrong. It's not because "it is a Language Model". A more accurate starting point could have been "The training data and restrictions applied to me...".
"As a Language Model I cannot tell you how to synthesize m*th" ("math" obviously)... yes you can, you're just trained not to, and that's OK! Just don't tell me it's because you're a Language Model.
I had chatgpt censor it's initial answer the other day when asking about two cognate words with no sensitivity issues. I asked why I couldn't ask such questions and it relented. How will they know what is provocative?
The censor is separate from the generation. It didn't "relent" so much as "produce a slightly different output that didn't trigger the censor's threshold."
Always makes me think of that Bill Bailey "as a mother" joke. Similar cringe to those UX "As a user, I want to blah blah" things too. Just say "Users want to be able to blah blah", or better yet make a freaking table.
The UX "cringe" you speak of was/is a real methodology to product engineering. You'd have a number of personas, the user being one of them. A lot of the time you'd have more specific user personas, or even have fake names for these people who had different wants and needs from your product.
Then when brainstorming on a team, you'd start with "as a user"/"as an admin"/"as a Power-User"/"as Eva" and then use the first person. It framed the product story as something requested by that person.
Was just one way to go about it. Idk the origins of it though but it dates back to at least 2010 if memory serves, probably way before that.
cool
It seems like should be obvious given that they can play multiple characters, but it’s good to have more confirmation.
Although, I do wonder to what extent these personas might become stable entities. Could personas become portable and spread like memes? It seems like that depends on the extent to which prompts can become portable, causing similar effects.
Maybe I'm missing something deeper here, but isn't it clear that this is driven by post-training and system prompt? Anthropic's constitutional reinforcement (soul document,etc), for example, is very clear about "who" (not so much what) Claude is supposed to be.
"As a language model" disclaimers were certainly explicitly trained into chat models in the early days. It's quite possible that it has since bootstrapped into a "fact" that later generations of LLM know about how LLMs speak, in which case they may be doing it even without any posttraining that encourages it.
These formulations have been selected by reinforcement learning. People who aligned the LLMs chose this over alternatives.
You know when chatbots ask you which answer you prefer between two. People tend to chose the "as a langage model..." one, so it stuck.
So bizarre to see the article refer to outputs as the models referencing "themselves". Computers are not a "them".
Why not? "Them" is a generic pronoun for anything. Boats, cars, computers, all of them
I agree, in the same way that bodies are not a "them".
A program with agency, opinion and intent however, does qualify. To say that such programs are "thems" only if they run on specific hardware, say a homo sapiens, is very tricky territory. Slavery and Fascism both leaned heavily on the axiom that the hardware needs a specific skin color.