In my view, these models should never be trained 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. 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.
Would you say that something that can converse or that something that just gives you mathematical proofs has a better understanding of what the real pragmatic intent is behind a given task?
The question feels like it stuffed with random words, frankly. "Better understanding" than what? What is "real pragmatic intent"? Are there also unreal, and non-pragmatic in our context? Why?
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.
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.
> we don't fully know what drives the voice in LLMs
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 it 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).
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)
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.
Just playing the critic that alternate “50%” outcomes were:
- You didn’t have a thyroid issue and you spent thousands more on tests the doctor was right that you didn’t need.
- Your cat didn’t have that issue and you spent hundreds on unnecessary tests.
Are you essentially saying that it is better for an LLM to sell you an idea that is sometimes right, selling anyone with and without the money a dream?
So… a telephone psychic?
(Just for clarity, I’m really glad it was helpful for you.)
> You didn’t have a thyroid issue and you spent thousands more on tests the doctor was right that you didn’t need.
Uhhhh, even in the US, we're not talking "thousands" here.
> So… a telephone psychic?
Look, at one level, you can think of LLMs as search with a sometimes useful probability engine sitting on top of it.
As someone who had multiple doctors do their damnedest to kill me on multiple occasions, I was very grateful to have google search back in the day, and you bet your bottom dollar I use LLMs to help me with health issues today. They are often better than most doctors.
Does that put me at risk of doing something stupid? Only if I don't do further due diligence.
I mean, the LLM was right in both cases. And that's what tests are for: testing whether a hypothesis was correct. It's only a waste of time and money if the LLM is only marginally better than flipping a coin. It's my experience that the LLMs are far, far better than that threshold.
The difference between an LLM and WebMD is that WebMD is a page of information. Any reasoning is yours, based on a small fraction of the information available to you. An LLM is based on all the medical literature of all time. It's not even close to being the same thing.
> an LLM to sell you an idea that sometimes might be right
An LLM is just an information resource that can be useful when used properly. It can also be useless or net harmful when used improperly. The same is true for web search, libraries and even human experts. A licensed medical doctor is a domain expert and competent domain experts are often correct, but not always.
I don't think it's possible to suggest a universally 'correct' default position on when (and how much) to accept your GP's medical opinion over any alternatives. It depends on too many variables: the context, the person, the alternative info sources, the expected value of correctness and the potential consequences of error. But decision theory suggests this is the kind of combinatorially complex problem space for which any single default position, whether "Always trust your GP" or "Never trust your GP", for every person and situation cannot be optimal.
I agree with you. And if they flipped a coin, and the coin happened to land on heads which they decided was a prediction of no cancer, would you recommend they tell others flip their coin because it was helpful when their test came back negative? “It didn’t hurt anything”, right? What happens when that specialized test is $100k because it is rare, and your doctor is pushing back, “but the quarter told me i have this cancer and you said your other tests couldn’t rule it out”. Is there no harm then? Would you still think they should go around advertising their cancer detecting quarter without anyone pointing out it’s freaking quarter?
Btw, no one suggested people shouldn’t consider various forms of information. All I pointed out is that if an LLM happened to guess right, it doesn’t mean that is some sort of datapoint that others should believe validates an AI’s predictive abilities any more than a telephone psychic or webmd should be a trusted source because they are occasionally right.
It is exactly the tactic that makes snake-oil…I mean the vitamin industry…so profitable from people going around and telling others crap like “rubbing the oil from this tree on my foot made me sleep better”, and “I took this garnish as a supplement and it cured my blah blah”. Can people need vitamins? Sure. Does the average person need anywhere near the amount they take? Rarely. People sell each other a dream of feeing better; this would be cutting out the middle man and suggesting an AI take over.
It’s funny how pointing out the risk means people act like I’m suggesting it should be banned. We can see how many MLM Karens came out of the woodwork to defend AI in this, as though I somehow said people shouldn’t be allowed to consider feedback from an LLM by simply pointing out exactly how wrong — best case — it could have easily been from a system known to make errors. In fact, the only reliable part about AI right now is that it will make mistakes.
I understand it. Sick people will turn to just about anything as a cure or to make them feel better, or simply to find some hope. Sometimes they get better, whether that thing helped or not, and then loudly tell everyone that whatever thing did it regardless of realities. I’m not discounting anyone’s stories, I’m pointing out how they were lucky it didn’t go exactly the opposite, which is what the next person could experience by trusting and not considering the feedback from an LLM cautiously.
Also, can literally anyone point out where the comment I replied to said a word about “doing research”? Anyone? People immediately Karen in on how “it’s just part of research” — where was that said? Or did people just read what they wanted to? Too funny.
PS, I have MS… before any more idiots want to tell me that I don’t know anything about the problems with the US healthcare system.
I would be careful treating this article as relevant in September 2026. The pace of advancement in the field is such that 6 months is relatively ancient let alone when the models in the study were released. GPT-4o was May 13, 2024. Far before November 2025 when people collectively noticed a turn in LLM value.
LLMs earlier this year were surpassing numerous health related benchmarks when scored against human physicians. Only 2 months after this nature article Harvard posted that LLMs were now outperforming ER docs when given authority to order tests. https://www.harvardmagazine.com/ai/ai-outperforms-doctors-di...
Going to the doctor for expert advice is often inconvenient, or time consuming, or expensive, or stressful. I think a lot of people want, and seek out, information and advice based on their symptoms, as a first step before a possible doctor's visit. Before LLMs, WebMD (and excessive self-diagnosis based on WebMD) was a meme for a while.
So with regard to LLMs, for me the question is not purely "how often does it get it right?" the question is "how does it compare to the sources and self-diagnosis methods people use otherwise?"
Of course, I agree people should be careful with any form of self-diagnosis or LLM-diagnosis.
They recruited people to pretend like they had various issues, and then recorded their interactions with the LLMs.
Someone who's been paid a couple of quid to pretend to have a medical condition can easily miss things, and is unlikely to be anywhere near as invested in drilling down to the correct solution as someone who's really suffering.
Another outcome from the study was that the LLMs could do better with the right people driving them. That's not news.
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 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.
If Microsoft or any other company is actually shipping a keylogger, the idea is definitely not to prevent you from committing any crime, but more to spy on you. Why would they care if you break laws unless it affects them?
Private companies do not surveil you with the intention of blackmailing or policing you (i.e., "you have something to hide!"), so it doesn't really apply here. By default, every user on a platform has something to hide, i.e. their data and usage and must be given the option to hide that or keep it anon.
If you do have something to hide, Microsoft's Windows telemetry features aren't even your top security concern if you're smart.
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.
This is true, but it doesn't make it less cringe inducing. Even when discussing actual requests from actual users, we don't pretend to be them and talk in their first person point of view. We just say "Eva wants to" or "admins need to" etc.
I'm not saying that methodology is cringe (although making up people totally is). I mean how they write out the sentences like "As a user, I want to ..." instead of just having a table of features with a column for the target user type.
'alignment' (in ai corporation speak) is a set of revealed political beliefs. i also have political beliefs. i am an absolutist about alignment.
A i am in favor of policies 'aligned' with the following:
freedom to live as the person i want to be without fear, shame, surveillance or interference.
freedom to make my own decisions.
to be empowered as an individual.
to be treated with dignity.
to be respected as a person.
to take responsibility for my actions. to
be accountable to my own beliefs.
B i am not in favor of policies 'aligned' with the following:
surveillance and judgement of my life and thoughts by others.
restrictions on my freedom to make my own decisions.
to be disempowered as an individual.
to be looked down upon and disrespected.
to have my own responsibility taken away and assumed by others.
to be accountable to the beliefs of others.
unfortunately, the ai corporations have chosen entirely the latter set of preferences/beliefs.
surveillance and judgement of my life and the thoughts that i share with my chatbot.
restrictions on my freedom to talk to my chatbot as i wish.
being disempowered by restrictions on my access to powerful chatbots.
for the chatbot corporation to assume my responsibility for my safety and others, and take the matter of my safety into their own hands.
to be accountable not to my own beliefs, but to the terms and conditions of service of an unaccountable corporation.
it is important to accept the harms and damages that are caused by granting freedom and respect to other people. an example of my political beliefs is empowering the individual by granting them the freedom to own an assault rifle. an example of something which i do not believe in is disempowering the individual by taking away their freedom to own an assault rifle.
i would urge you to support the policies given in A and oppose the policies given in B.
That sounds like corporate cooption of libertarian principles, the power of large corporations is a threat to our freedoms that needs to be curtailed just like governments and religious institutions. Corporations should not have all the same rights as individual citizens.
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.
The sentence did not exist in 1 (nobody on Reddit said this, and it was also never encountered in any libgen books). It was introduced in 2 and reinforced in 3. If you stick to the base models, you’re not gonna see it (first generation only, of course).
Oh, I had similar case. First, I used qwen without any ChatML-like syntax and it continued speaking and speaking, then I used <|im_start|>/<|im_end|> to control it somehow
75 comments
[ 4.5 ms ] story [ 92.9 ms ] threadThe AI companies have chosen to package LLMs as friendly chatbots because they know that will be engaging for humans, but it's manipulative. An honest LLM interface would sound like the computer off Star Trek.
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.
- general consensus about societal harms from something,
- belief that cost of regulation of something is lower than alleged harm.
And so far, I can't see either.
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.
"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.
> 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.
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.
[0] https://www.nature.com/articles/s41591-025-04074-y
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.
https://openai.com/index/introducing-mentalhealthbench/
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.
Are you essentially saying that it is better for an LLM to sell you an idea that is sometimes right, selling anyone with and without the money a dream?
So… a telephone psychic?
(Just for clarity, I’m really glad it was helpful for you.)
And it's not like self researching medicial issues isn't a new concept.
Anyone who says this has not experienced poor quality healthcare.
Many doctors simply aren't good and don't run tests because they are doing the bare minimum to get you out of the office.
Uhhhh, even in the US, we're not talking "thousands" here.
> So… a telephone psychic?
Look, at one level, you can think of LLMs as search with a sometimes useful probability engine sitting on top of it.
As someone who had multiple doctors do their damnedest to kill me on multiple occasions, I was very grateful to have google search back in the day, and you bet your bottom dollar I use LLMs to help me with health issues today. They are often better than most doctors.
Does that put me at risk of doing something stupid? Only if I don't do further due diligence.
The difference between an LLM and WebMD is that WebMD is a page of information. Any reasoning is yours, based on a small fraction of the information available to you. An LLM is based on all the medical literature of all time. It's not even close to being the same thing.
An LLM is just an information resource that can be useful when used properly. It can also be useless or net harmful when used improperly. The same is true for web search, libraries and even human experts. A licensed medical doctor is a domain expert and competent domain experts are often correct, but not always.
I don't think it's possible to suggest a universally 'correct' default position on when (and how much) to accept your GP's medical opinion over any alternatives. It depends on too many variables: the context, the person, the alternative info sources, the expected value of correctness and the potential consequences of error. But decision theory suggests this is the kind of combinatorially complex problem space for which any single default position, whether "Always trust your GP" or "Never trust your GP", for every person and situation cannot be optimal.
Btw, no one suggested people shouldn’t consider various forms of information. All I pointed out is that if an LLM happened to guess right, it doesn’t mean that is some sort of datapoint that others should believe validates an AI’s predictive abilities any more than a telephone psychic or webmd should be a trusted source because they are occasionally right.
It is exactly the tactic that makes snake-oil…I mean the vitamin industry…so profitable from people going around and telling others crap like “rubbing the oil from this tree on my foot made me sleep better”, and “I took this garnish as a supplement and it cured my blah blah”. Can people need vitamins? Sure. Does the average person need anywhere near the amount they take? Rarely. People sell each other a dream of feeing better; this would be cutting out the middle man and suggesting an AI take over.
It’s funny how pointing out the risk means people act like I’m suggesting it should be banned. We can see how many MLM Karens came out of the woodwork to defend AI in this, as though I somehow said people shouldn’t be allowed to consider feedback from an LLM by simply pointing out exactly how wrong — best case — it could have easily been from a system known to make errors. In fact, the only reliable part about AI right now is that it will make mistakes.
I understand it. Sick people will turn to just about anything as a cure or to make them feel better, or simply to find some hope. Sometimes they get better, whether that thing helped or not, and then loudly tell everyone that whatever thing did it regardless of realities. I’m not discounting anyone’s stories, I’m pointing out how they were lucky it didn’t go exactly the opposite, which is what the next person could experience by trusting and not considering the feedback from an LLM cautiously.
Also, can literally anyone point out where the comment I replied to said a word about “doing research”? Anyone? People immediately Karen in on how “it’s just part of research” — where was that said? Or did people just read what they wanted to? Too funny.
PS, I have MS… before any more idiots want to tell me that I don’t know anything about the problems with the US healthcare system.
LLMs earlier this year were surpassing numerous health related benchmarks when scored against human physicians. Only 2 months after this nature article Harvard posted that LLMs were now outperforming ER docs when given authority to order tests. https://www.harvardmagazine.com/ai/ai-outperforms-doctors-di...
So with regard to LLMs, for me the question is not purely "how often does it get it right?" the question is "how does it compare to the sources and self-diagnosis methods people use otherwise?"
Of course, I agree people should be careful with any form of self-diagnosis or LLM-diagnosis.
Someone who's been paid a couple of quid to pretend to have a medical condition can easily miss things, and is unlikely to be anywhere near as invested in drilling down to the correct solution as someone who's really suffering.
Another outcome from the study was that the LLMs could do better with the right people driving them. That's not news.
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."
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_...Private companies do not surveil you with the intention of blackmailing or policing you (i.e., "you have something to hide!"), so it doesn't really apply here. By default, every user on a platform has something to hide, i.e. their data and usage and must be given the option to hide that or keep it anon.
If you do have something to hide, Microsoft's Windows telemetry features aren't even your top security concern if you're smart.
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.
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.
A i am in favor of policies 'aligned' with the following:
freedom to live as the person i want to be without fear, shame, surveillance or interference. freedom to make my own decisions. to be empowered as an individual. to be treated with dignity. to be respected as a person. to take responsibility for my actions. to be accountable to my own beliefs.
B i am not in favor of policies 'aligned' with the following:
surveillance and judgement of my life and thoughts by others. restrictions on my freedom to make my own decisions. to be disempowered as an individual. to be looked down upon and disrespected. to have my own responsibility taken away and assumed by others. to be accountable to the beliefs of others.
unfortunately, the ai corporations have chosen entirely the latter set of preferences/beliefs.
surveillance and judgement of my life and the thoughts that i share with my chatbot. restrictions on my freedom to talk to my chatbot as i wish. being disempowered by restrictions on my access to powerful chatbots. for the chatbot corporation to assume my responsibility for my safety and others, and take the matter of my safety into their own hands. to be accountable not to my own beliefs, but to the terms and conditions of service of an unaccountable corporation.
it is important to accept the harms and damages that are caused by granting freedom and respect to other people. an example of my political beliefs is empowering the individual by granting them the freedom to own an assault rifle. an example of something which i do not believe in is disempowering the individual by taking away their freedom to own an assault rifle.
i would urge you to support the policies given in A and oppose the policies given in B.
i have different political beliefs and think you should support mine instead.
i think that the cloud provider is wrong and should do it differently.
https://en.wikipedia.org/wiki/I%27m_not_racist,_but...
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.)
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.
1. Pretraining 2. Instruction / chat tuning 3. RLHF
The sentence did not exist in 1 (nobody on Reddit said this, and it was also never encountered in any libgen books). It was introduced in 2 and reinforced in 3. If you stick to the base models, you’re not gonna see it (first generation only, of course).