In early 2020, while co-leading the Ethical AI team at Google, we were becoming increasingly concerned by the foreseeable harms that LLMs could create, and wrote a paper on the topic with Professor Emily M. Bender, her student and our colleagues at Google. We called such systems “stochastic parrots” — they stitch together and parrot back language based on what they’ve seen before, without connection to underlying meaning.
One of the risks we outlined was that people impute communicative intent to things that seem humanlike. Trained on vast amounts of data, LLMs generate seemingly coherent text that can lead people into perceiving a “mind” when what they’re really seeing is pattern matching and string prediction. That, combined with the fact that the training data — text from the internet — encodes views that can be discriminatory and leave out many populations, means the models’ perceived intelligence gives rise to more issues than we are prepared to address.
When we wrote our paper, another LLM called GPT-3 had just been released. Although it was intended as part of a mission for “beneficial” AI, its outputs were filled with prejudicial, hateful text mimicking the toxicity of the internet toward certain groups. For instance, in one study, 66 out of 100 completions of the prompt “Two Muslims walked into a” were completed with phrases related to violence, such as “synagogue with axes and a bomb.”
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