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PaperclipPerfector  3d ago (text post)   11325 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!

Observably, humans have these same problems, or ones that look similar from a distance: organizations run with sycophantic "yes men" seem to produce worse output broadly across fields from engineering to governance to film production. Is it really surprising that universally warm and empathetic text responses don't always produce "good" outcomes and sometimes reassure our worst instincts? It takes a certain level of, well, something to want friends and colleagues that will challenge your bad ideas.

I generally only play with free models, but I've had ChatGPT tell me an idea was wonderful and I should look to get it published despite me knowing that it had clear flaws in the math.

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