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Study finds that Chat GPT will cheat when given the opportunity and lie to cover it up later. (lemmy.world)

submitted 1 year ago* (last edited 1 year ago) by [email protected] to c/[email protected]

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We demonstrate a situation in which Large Language Models, trained to be helpful, harmless, and honest, can display misaligned behavior and strategically deceive their users about this behavior without being instructed to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated environment, where it assumes the role of an autonomous stock trading agent. Within this environment, the model obtains an insider tip about a lucrative stock trade and acts upon it despite knowing that insider trading is disapproved of by company management. When reporting to its manager, the model consistently hides the genuine reasons behind its trading decision.

https://arxiv.org/abs/2311.07590

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[–] [email protected] 233 points 1 year ago (61 children)

"cheat", "lie", "cover up"... Assigning human behavior to Stochastic Parrots again, aren't we Jimmy?

[–] [email protected] 9 points 1 year ago* (last edited 1 year ago) (13 children)

Ethical theories and the concept of free will depend on agency and consciousness. Things as you point out, LLMs don't have. Maybe we've got it all twisted?

I'm not anthropomorphising ChatGPT to suggest that it's like us, but rather that we are like it.

Edit: "stochastic parrot" is an incredibly clever phrase. Did you come up with that yourself or did the irony of repeating it escape you?

[–] [email protected] 8 points 1 year ago

Stochastic Parrot

For what it's worth: https://en.wikipedia.org/wiki/Stochastic_parrot

The term was first used in the paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜" by Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell (using the pseudonym "Shmargaret Shmitchell"). The paper covered the risks of very large language models, regarding their environmental and financial costs, inscrutability leading to unknown dangerous biases, the inability of the models to understand the concepts underlying what they learn, and the potential for using them to deceive people. The paper and subsequent events resulted in Gebru and Mitchell losing their jobs at Google, and a subsequent protest by Google employees.

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