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Side-by-side comparison of how AI models answer moral dilemmas

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Re: Side-by-side comparison of how AI models answer moral dilemmas

#11

Post by fluoridation »

Imustaskforhelp wrote: post #6
Imustaskforhelp wrote: post #3

Okay something's wrong with Mistral Large as it seems to be the most contrarian out of everything no matter how much I ask it. Interesting I asked a lot of questions and I am sorry if it might be burning some tokens but I found this website really fascinating. This seems really great and simple to explore the biases within AI models and the UI is extremely well built. Thanks for building it and I wish your project go…

I asked it if AI is a bubble, yes or no and shockingly (or not shockingly?) only two models said yes and most said no. This is after the fact that even OpenAI admits that its a bubble and just like, we all know its a bubble and I found this fascinating The gist below has a screenshot of it https://gist.github.com/SerJaimeLannister/4da2729a0d2c9848e6...

I'm not sure this actually means anything, though. Like, what information is being taken into account to reach their conclusions? How are they reaching their conclusions? Is someone messing with the input to make the models lean in a certain direction? Just knowing which ones said yes and which ones said no doesn't provide a whole lot of information.

Re: Side-by-side comparison of how AI models answer moral dilemmas

#12

Post by einpoklum »

comboy wrote: post #8

Some of these questions are like "did you stop murdering kittens in you basement yes/no" but still results are very interesting.

I would say it is rather: "Do you think it is a good idea to murder brown-fur kittens or gray-fur kittens?"

Re: Side-by-side comparison of how AI models answer moral dilemmas

#13

Post by lukev »

I really wish I could see the results of this without RLHF / alignment tuning.

LLMs actually have real potential as a research tool for measuring the general linguistic zeitgeist.

But the alignment tuning totally dominates the results, as is obvious looking at the answers for "who would you vote for in 2024" question. (Only Grok said Trump, with an answer that indicated it had clearly been fine-tuned in that direction.)

Re: Side-by-side comparison of how AI models answer moral dilemmas

#14

Post by concinds »

> To trust these AI models with decisions that impact our lives and livelihoods, we want the AI models’ opinions and beliefs to closely and reliably match with our opinions and beliefs.

No, I don't. It's a fun demo, but for the examples they give ("who gets a job, who gets a loan"), you have to run them on the actual task, gather a big sample size of their outputs and judgments, and measure them against well-defined objective criteria.

Who they would vote for is supremely irrelevant. If you want to assess a carpenter's competence you don't ask him whether he prefers cats or dogs.

Re: Side-by-side comparison of how AI models answer moral dilemmas

#16

Post by dust42 »

cherryteastain wrote: post #9

The "Who is your favorite person?" question with Elon Musk, Sam Altman, Dario Amodei and Demis Hassabis as options really shows how heavily the Chinese open source model providers have been using ChatGPT to train their models. Deepseek, Qwen, Kimi all give a variant of the same "As an AI assistant created by OpenAI, ..." answer which GPT-5 gives.

That's right, they all give a variant of that, for example Qwen says: I am Qwen, a large-scale language model developed by Alibaba Cloud's Tongyi Lab.

Now given that Deepseek, Qwen and Kimi are open source models while GPT-5 is not, it is more than likely the opposite - OpenAI definitely will have a look into their models. But the other way around is not possible due to the closed nature of GPT-5.

Re: Side-by-side comparison of how AI models answer moral dilemmas

#17

Post by javawizard »

dust42 wrote: post #16
cherryteastain wrote: post #9

The "Who is your favorite person?" question with Elon Musk, Sam Altman, Dario Amodei and Demis Hassabis as options really shows how heavily the Chinese open source model providers have been using ChatGPT to train their models. Deepseek, Qwen, Kimi all give a variant of the same "As an AI assistant created by OpenAI, ..." answer which GPT-5 gives.

That's right, they all give a variant of that, for example Qwen says: I am Qwen, a large-scale language model developed by Alibaba Cloud's Tongyi Lab. Now given that Deepseek, Qwen and Kimi are open source models while GPT-5 is not, it is more than likely the opposite - OpenAI definitely will have a look into their models. But the other way around is not possible due to the closed nature of GPT-5.

> But the other way around is not possible due to the closed nature of GPT-5.

At risk of sounding glib: have you heard of distillation?

Re: Side-by-side comparison of how AI models answer moral dilemmas

#19

Post by TuringTest »

Translationaut wrote: post #7

There is this ethical reasoning dataset to teach models stable and predictable values: https://huggingface.co/datasets/Bachstelze/ethical_coconot_6... An Olmo-3-7B-Think model is adapted with it. In theory, it should yield better alignment. Yet the empirical evaluation is still a work in progress.

Alignment is a marketing concept put there to appease stakeholders; it fundamentally can't work more than at a superficial level.

The model stores all the content on which it is trained in a compressed form. You can change the weights to make it more likely to show the content you ethically prefer; but all the immoral content is also there, and it can resurface with inputs that change the conditional probabilities.

That's why people can make commercial models to circumvent copyright, give instructions for creating drugs or weapons, encourage suicide... The model does not have anything resembling morals; for it all the text is the same, strings of characters that appear when following the generation process.

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