Web: http://arxiv.org/abs/2206.08325

June 17, 2022, 1:12 a.m. | Maribeth Rauh, John Mellor, Jonathan Uesato, Po-Sen Huang, Johannes Welbl, Laura Weidinger, Sumanth Dathathri, Amelia Glaese, Geoffrey Irving, Iason G

cs.CL updates on arXiv.org arxiv.org

Large language models produce human-like text that drive a growing number of
applications. However, recent literature and, increasingly, real world
observations, have demonstrated that these models can generate language that is
toxic, biased, untruthful or otherwise harmful. Though work to evaluate
language model harms is under way, translating foresight about which harms may
arise into rigorous benchmarks is not straightforward. To facilitate this
translation, we outline six ways of characterizing harmful text which merit
explicit consideration when designing new benchmarks. …

arxiv benchmarking language language models models text

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