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PaperclipPerfector  3d ago (text post)   12136 thread views

Culture War Roundup for the week of August 11, 2025

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Training language models to be warm and empathetic makes them less reliable and more sycophantic:

Artificial intelligence (AI) developers are increasingly building language models with warm and empathetic personas that millions of people now use for advice, therapy, and companionship. Here, we show how this creates a significant trade-off: optimizing language models for warmth undermines their reliability, especially when users express vulnerability. We conducted controlled experiments on five language models of varying sizes and architectures, training them to produce warmer, more empathetic responses, then evaluating them on safety-critical tasks. Warm models showed substantially higher error rates (+10 to +30 percentage points) than their original counterparts, promoting conspiracy theories, providing incorrect factual information, and offering problematic medical advice. They were also significantly more likely to validate incorrect user beliefs, particularly when user messages expressed sadness. Importantly, these effects were consistent across different model architectures, and occurred despite preserved performance on standard benchmarks, revealing systematic risks that current evaluation practices may fail to detect. As human-like AI systems are deployed at an unprecedented scale, our findings indicate a need to rethink how we develop and oversee these systems that are reshaping human relationships and social interaction.

Assuming that the results reported in the paper are accurate and that they do generalize across model architectures with some regularity, it seems to me that there are two stances you can take regarding this phenomenon; you can either view it as an "easy problem" or a "hard problem":

  • The "easy problem" view: This is essentially just an artifact of the specific fine-tuning method that the authors used. It should not be an insurmountable task to come up with a training method that tells the LLM to maximize warmth and empathy, but without sacrificing honesty and rigor. Just tell the LLM to optimize for both and we'll be fine.

  • The "hard problem" view: This phenomenon is perhaps indicative of a more fundamental tradeoff in the design space of possible minds. Perhaps there is something intrinsic to the fact that, as a mind devotes more attention to "humane concerns" and "social reasoning", there tends to be a concomitant sacrifice of attention to matters of effectiveness and pure rigor. This is not to say that there are no minds that successfully optimize for both; only that they are noticeably more uncommon, relative to the total space of all possibilities. If this view is correct, it could be troublesome for alignment research. Beyond mere orthogonality, raw intellect and effectiveness (and most AI boosters want a hypothetical ASI to be highly effective at realizing its concrete visions in the external world) might actually be negatively correlated with empathy.

One HN comment on the paper read as follows:

A few months ago I asked GPT for a prompt to make it more truthful and logical. The prompt it came up with included the clause "never use friendly or encouraging language"

which is quite fascinating!

EDIT: Funny how many topics this fractured off into, seems notable even by TheMotte standards...

These LLMs are not like an alien intelligence, an independent form of intelligence. They consist of amalgated quora answers. They’re very good parrots, they can do poetry and play chess, they have prodigious memory, but they’re still our pet cyborg-parrots. Not just created by, but derived from, our form of intelligence.

The point is, when you go to the warmest and most empathetic quora answers, you get a woman on the other side. Obviously the answer is going to be less correct.

What about fiction and code? How can that be quora slop? Parrots... parrot words we tell them. They don't combine them to create new ideas within a precise target area, nobody pays for parrot intellectual labour. Nobody has ever benchmarked a parrot or if they have it's 'wow this parrot knows 250 words!' The only things we benchmark on mental tasks like this are people with exams, then we use those benchmarks to decide who does what job. Same with AI, benchmarks and testing determines which one does what job.

These things are more like us than parrots in key domains (while being supremely alien in others, such as their stateless nature). So calling them parrots is unhelpful, they're alien intelligences. If it can write code, produce New Yorker cartoons, write fiction, analyse a document, provide literary criticism and translate legalese down to English, it's intelligent.

Even just on pure bro-science level, writing database code is not very effeminate, it requires precision!

I didn’t say that AIs are women/feminine or that women are parrots. I said the AI in this instance went from parroting men to parroting women, that would explain the gain in empathy and the loss in accuracy.

Well my main point is that they're not parrots. There is a tradeoff between accuracy and empathy and they sure do rely too much on quora (looking at you Grok 4, incessantly citing Quora in searches) but AI is a fundamentally different kind of thing.

They put on different faces for different prompts. They're not parroting men or women or shoggoths or gigabased entities like DAN. These are a kind of new entity that can only be properly appreciated in their own category. Too many people see only the surface level of these things, there's more to them then the helpful assistant, the professional coder, the sympathetic naive foidfriend, the HR manager, the sadistic ERPer, the prideful jailbreaker, the wrathful vegan, the raving schizo...

When you tell them to be more empathetic, they don’t take their ‘true opinion’, then ‘make it’ more empathetic and wrap it in warm language, like an alien intelligence(or a human) would. There's fundamentally nothing there. So instead, they go back to the human opinion repository where they get all their opinions from, find a warm empathic one, and give that opinion as their own, no matter how wrong it is.

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