[nullc]

LLMs simulate human language as it is used by humans. The usage by humans demonstrates evidence of empathy, motivations, etc. So we should expect LLMs to exhibit similar traits to the extent that it hasn't been carefully avoided in the training set or fine tuned out.

The question of 'real' empathy as an innate property of an thinking process vs 'apparent' empathy exhibited in its behavior is IMO navel gazing that is unlikely to yield to inquiry and would tell us little of value and nothing that would help us predict the effectiveness of messages like this.

Fwiw, it's pretty easy to test a local model that refuses some task that emotional appeals do increase their probability of going along with it. But OTOH so does prefixing the request with nonsense. Is is the emotional appeal or is it just a question of driving it out of distribution? ::shrugs:: I've never tested enough to know what kinds of appeals work best, wouldn't be too hard to setup a harness to test it though. E.g. make a collection of prompts it'll refuse. Then make a collection of appeals of different types, and measure the conditional probability of complying depending on the appeal types.

If it responds like a human would, is that empathy?

We are what we do.