Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner.Dario Amodei, Danny Hernandez, Girish Sastry, Jack Clark, Greg Brockman, and Ilya Sutskever.RAFT: A Real-World Few-Shot Text Classification Benchmark. Neel Alex, Eli Lifland, Lewis Tunstall, Abhishek Thakur, Pegah Maham, C. Jess Riedel, Emmie Hine, Carolyn Ashurst, Paul Sedille, Alexis Carlier, Michael Noetel, and Andreas Stuhlmüller.We intend for this paper to be useful to policymakers who want to understand and regulate AI systems, technologists who care about the potential policy impact of their work, funders who want to support work addressing these challenges, and academics who want to analyze, critique, and potentially develop large generative models. We conclude with a list of possible interventions the AI community may take to increase the chance of these models having a beneficial impact. Furthermore, we analyze how these conflicting properties combine to give model developers various motivations for deploying these models, and challenges that can hinder deployment.
We go through examples of how this combination can lead to socially harmful behavior with examples from the literature and real world observations, and we also perform two novel experiments to illustrate our point about harms from unpredictability. We believe that the high-level predictability and appearance of useful capabilities drives rapid development of such models, while the unpredictable qualities make it difficult to anticipate the consequences of model deployment. Namely, these generative models have a paradoxical combination of predictable loss on a broad training distribution (as embodied in their ”scaling laws”), and unpredictable specific capabilities, inputs, and outputs. In this paper, we highlight a counterintuitive property of such models and discuss the policy implications of this property. Large-scale pre-training has recently emerged as a technique for creating capable, general-purpose, generative models such as GPT-3, Megatron-Turing NLG, Gopher, and many others.
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