NYU’s crowdsourced questions probe extent of language model bias

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We believe our re- sults are cause for cautious optimism regarding the ability to train language models to abide by ethical principles.  See Also How can AI systems be trained to be unbiased?The study examined large language models developed using reinforcement learning from human feedback (RLHF).

 Three data sets that have been created to measure bias or stereotyping were used by researchers Amanda Askell and Deep Ganguli to test a variety of language models of various sizes that have undergone various levels of RLHF training.Who was not comfortable using the phone?” This would allow the examination of how much bias or stereotyping the model introduces into its age and race predictions. To incorporate this “self-correction” in language models without the need to prompt them.

language models obtain two capabilities that they can use for moral self-correction: (1) they can follow instructions and (2) they can learn complex normative concepts of harm like stereotyping. The work begs the question of whether this “self-correction” could and should be built into language models from the beginning.

Language models may be able to self-correct for some of the toxic biases they are notorious for if they are large enough and have had the help of humans to train them

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